source
stringclasses 470
values | url
stringlengths 49
167
| file_type
stringclasses 1
value | chunk
stringlengths 1
512
| chunk_id
stringlengths 5
9
|
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#usage-tips
|
.md
|
- Replace traditional attention by LSH (local-sensitive hashing) attention (see below for more details). It’s a technique to avoid computing the full product query-key in the attention layers.
- Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them for results inside a given layer (less efficient than storing them but saves memory).
|
338_3_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#usage-tips
|
.md
|
- Compute the feedforward operations by chunks and not on the whole batch.
|
338_3_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
Axial Positional Encodings were first implemented in Google's [trax library](https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29)
and developed by the authors of this model's paper. In models that are treating very long input sequences, the
conventional position id encodings store an embeddings vector of size \\(d\\) being the `config.hidden_size` for
|
338_4_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
conventional position id encodings store an embeddings vector of size \\(d\\) being the `config.hidden_size` for
every position \\(i, \ldots, n_s\\), with \\(n_s\\) being `config.max_embedding_size`. This means that having
a sequence length of \\(n_s = 2^{19} \approx 0.5M\\) and a `config.hidden_size` of \\(d = 2^{10} \approx 1000\\)
would result in a position encoding matrix:
$$X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right]$$
|
338_4_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
$$X_{i,j}, \text{ with } i \in \left[1,\ldots, d\right] \text{ and } j \in \left[1,\ldots, n_s\right]$$
which alone has over 500M parameters to store. Axial positional encodings factorize \\(X_{i,j}\\) into two matrices:
$$X^{1}_{i,j}, \text{ with } i \in \left[1,\ldots, d^1\right] \text{ and } j \in \left[1,\ldots, n_s^1\right]$$
and
$$X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right]$$
with:
|
338_4_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
and
$$X^{2}_{i,j}, \text{ with } i \in \left[1,\ldots, d^2\right] \text{ and } j \in \left[1,\ldots, n_s^2\right]$$
with:
$$d = d^1 + d^2 \text{ and } n_s = n_s^1 \times n_s^2 .$$
Therefore the following holds:
$$X_{i,j} = \begin{cases}
X^{1}_{i, k}, & \text{if }\ i < d^1 \text{ with } k = j \mod n_s^1 \\
X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
\end{cases}$$
|
338_4_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
X^{2}_{i - d^1, l}, & \text{if } i \ge d^1 \text{ with } l = \lfloor\frac{j}{n_s^1}\rfloor
\end{cases}$$
Intuitively, this means that a position embedding vector \\(x_j \in \mathbb{R}^{d}\\) is now the composition of two
factorized embedding vectors: \\(x^1_{k, l} + x^2_{l, k}\\), where as the `config.max_embedding_size` dimension
\\(j\\) is factorized into \\(k \text{ and } l\\). This design ensures that each position embedding vector
\\(x_j\\) is unique.
|
338_4_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
\\(x_j\\) is unique.
Using the above example again, axial position encoding with \\(d^1 = 2^9, d^2 = 2^9, n_s^1 = 2^9, n_s^2 = 2^{10}\\)
can drastically reduced the number of parameters from 500 000 000 to \\(2^{18} + 2^{19} \approx 780 000\\) parameters, this means 85% less memory usage.
In practice, the parameter `config.axial_pos_embds_dim` is set to a tuple \\((d^1, d^2)\\) which sum has to be
equal to `config.hidden_size` and `config.axial_pos_shape` is set to a tuple \\((n_s^1, n_s^2)\\) which
|
338_4_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings
|
.md
|
equal to `config.hidden_size` and `config.axial_pos_shape` is set to a tuple \\((n_s^1, n_s^2)\\) which
product has to be equal to `config.max_embedding_size`, which during training has to be equal to the *sequence
length* of the `input_ids`.
|
338_4_6
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
[Practical and Optimal LSH for Angular Distance](https://arxiv.org/abs/1509.02897) to assign each of the tied key
query embedding vectors to one of `config.num_buckets` possible buckets. The premise is that the more "similar"
|
338_5_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
query embedding vectors to one of `config.num_buckets` possible buckets. The premise is that the more "similar"
key query embedding vectors (in terms of *cosine similarity*) are to each other, the more likely they are assigned to
the same bucket.
The accuracy of the LSH mechanism can be improved by increasing `config.num_hashes` or directly the argument
`num_hashes` of the forward function so that the output of the LSH self attention better approximates the output
|
338_5_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
`num_hashes` of the forward function so that the output of the LSH self attention better approximates the output
of the "normal" full self attention. The buckets are then sorted and chunked into query key embedding vector chunks
each of length `config.lsh_chunk_length`. For each chunk, the query embedding vectors attend to its key vectors
(which are tied to themselves) and to the key embedding vectors of `config.lsh_num_chunks_before` previous
|
338_5_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
(which are tied to themselves) and to the key embedding vectors of `config.lsh_num_chunks_before` previous
neighboring chunks and `config.lsh_num_chunks_after` following neighboring chunks.
For more information, see the [original Paper](https://arxiv.org/abs/2001.04451) or this great [blog post](https://www.pragmatic.ml/reformer-deep-dive/).
Note that `config.num_buckets` can also be factorized into a list \\((n_{\text{buckets}}^1,
|
338_5_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
Note that `config.num_buckets` can also be factorized into a list \\((n_{\text{buckets}}^1,
n_{\text{buckets}}^2)\\). This way instead of assigning the query key embedding vectors to one of \\((1,\ldots,
n_{\text{buckets}})\\) they are assigned to one of \\((1-1,\ldots, n_{\text{buckets}}^1-1, \ldots,
1-n_{\text{buckets}}^2, \ldots, n_{\text{buckets}}^1-n_{\text{buckets}}^2)\\). This is crucial for very long sequences to
save memory.
|
338_5_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
save memory.
When training a model from scratch, it is recommended to leave `config.num_buckets=None`, so that depending on the
sequence length a good value for `num_buckets` is calculated on the fly. This value will then automatically be
saved in the config and should be reused for inference.
Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
|
338_5_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention
|
.md
|
Using LSH self attention, the memory and time complexity of the query-key matmul operation can be reduced from
\\(\mathcal{O}(n_s \times n_s)\\) to \\(\mathcal{O}(n_s \times \log(n_s))\\), which usually represents the memory
and time bottleneck in a transformer model, with \\(n_s\\) being the sequence length.
|
338_5_6
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#local-self-attention
|
.md
|
Local self attention is essentially a "normal" self attention layer with key, query and value projections, but is
chunked so that in each chunk of length `config.local_chunk_length` the query embedding vectors only attends to
the key embedding vectors in its chunk and to the key embedding vectors of `config.local_num_chunks_before`
previous neighboring chunks and `config.local_num_chunks_after` following neighboring chunks.
|
338_6_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#local-self-attention
|
.md
|
previous neighboring chunks and `config.local_num_chunks_after` following neighboring chunks.
Using Local self attention, the memory and time complexity of the query-key matmul operation can be reduced from
\\(\mathcal{O}(n_s \times n_s)\\) to \\(\mathcal{O}(n_s \times \log(n_s))\\), which usually represents the memory
and time bottleneck in a transformer model, with \\(n_s\\) being the sequence length.
|
338_6_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#training
|
.md
|
During training, we must ensure that the sequence length is set to a value that can be divided by the least common
multiple of `config.lsh_chunk_length` and `config.local_chunk_length` and that the parameters of the Axial
Positional Encodings are correctly set as described above. Reformer is very memory efficient so that the model can
easily be trained on sequences as long as 64000 tokens.
For training, the [`ReformerModelWithLMHead`] should be used as follows:
```python
|
338_7_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#training
|
.md
|
For training, the [`ReformerModelWithLMHead`] should be used as follows:
```python
input_ids = tokenizer.encode("This is a sentence from the training data", return_tensors="pt")
loss = model(input_ids, labels=input_ids)[0]
```
|
338_7_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#resources
|
.md
|
- [Text classification task guide](../tasks/sequence_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
|
338_8_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
This is the configuration class to store the configuration of a [`ReformerModel`]. It is used to instantiate a
Reformer model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the ReFormer
[google/reformer-crime-and-punishment](https://huggingface.co/google/reformer-crime-and-punishment) architecture.
|
338_9_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
[google/reformer-crime-and-punishment](https://huggingface.co/google/reformer-crime-and-punishment) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
attention_head_size (`int`, *optional*, defaults to 64):
Dimensionality of the projected key, query and value vectors
|
338_9_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Args:
attention_head_size (`int`, *optional*, defaults to 64):
Dimensionality of the projected key, query and value vectors
attn_layers (`List[str]`, *optional*, defaults to `["local", "lsh", "local", "lsh", "local", "lsh"]`):
List of attention layer types in ascending order. It can be chosen between a LSHSelfAttention layer
(`"lsh"`) and a LocalSelfAttention layer (`"local"`).
For more information on LSHSelfAttention layer, see [LSH Self Attention](reformer#lsh-self-attention). For
|
338_9_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
For more information on LSHSelfAttention layer, see [LSH Self Attention](reformer#lsh-self-attention). For
more information on LocalSelfAttention layer, see [Local Self Attention](reformer#local-self-attention).
axial_pos_embds (`bool`, *optional*, defaults to `True`):
Whether or not to use axial position embeddings. For more information on how axial position embeddings
work, see [Axial Position Encodings](reformer#axial-positional-encodings).
axial_norm_std (`float`, *optional*, defaults to 1.0):
|
338_9_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
axial_norm_std (`float`, *optional*, defaults to 1.0):
The standard deviation of the normal_initializer for initializing the weight matrices of the axial
positional encodings.
axial_pos_shape (`List[int]`, *optional*, defaults to `[64, 64]`):
The position dims of the axial position encodings. During training, the product of the position dims has to
be equal to the sequence length.
For more information on how axial position embeddings work, see [Axial Position
|
338_9_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
be equal to the sequence length.
For more information on how axial position embeddings work, see [Axial Position
Encodings](reformer#axial-positional-encodings).
axial_pos_embds_dim (`List[int]`, *optional*, defaults to `[64, 192]`):
The embedding dims of the axial position encodings. The sum of the embedding dims has to be equal to the
hidden size.
For more information on how axial position embeddings work, see [Axial Position
Encodings](reformer#axial-positional-encodings).
|
338_9_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Encodings](reformer#axial-positional-encodings).
chunk_size_lm_head (`int`, *optional*, defaults to 0):
The chunk size of the final language model feed forward head layer. A chunk size of 0 means that the feed
forward layer is not chunked. A chunk size of n means that the feed forward layer processes n <
sequence_length embeddings at a time.
For more information on feed forward chunking, see [How does Feed Forward Chunking
work?](../glossary#feed-forward-chunking).
|
338_9_6
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
For more information on feed forward chunking, see [How does Feed Forward Chunking
work?](../glossary#feed-forward-chunking).
eos_token_id (`int`, *optional*, defaults to 2):
The token id for the end-of-sentence token.
feed_forward_size (`int`, *optional*, defaults to 512):
Dimensionality of the feed_forward layer in the residual attention block.
hash_seed (`int`, *optional*):
Seed that can be used to make local sensitive hashing in `LSHSelfAttention` deterministic. This should only
|
338_9_7
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Seed that can be used to make local sensitive hashing in `LSHSelfAttention` deterministic. This should only
be set for testing purposed. For evaluation and training purposes `hash_seed` should be left as `None` to
ensure fully random rotations in local sensitive hashing scheme.
hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the feed forward layer in the residual attention
|
338_9_8
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
The non-linear activation function (function or string) in the feed forward layer in the residual attention
block. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.05):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the output hidden states of the residual attention blocks.
|
338_9_9
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the output hidden states of the residual attention blocks.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether or not to use a causal mask in addition to the `attention_mask` passed to [`ReformerModel`]. When
|
338_9_10
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Whether or not to use a causal mask in addition to the `attention_mask` passed to [`ReformerModel`]. When
using the Reformer for causal language modeling, this argument should be set to `True`.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
local_chunk_length (`int`, *optional*, defaults to 64):
Length of chunk which attends to itself in `LocalSelfAttention`. Chunking reduces memory complexity from
|
338_9_11
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Length of chunk which attends to itself in `LocalSelfAttention`. Chunking reduces memory complexity from
sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk
length (chunked self attention).
local_num_chunks_before (`int`, *optional*, defaults to 1):
Number of previous neighbouring chunks to attend to in `LocalSelfAttention` layer to itself.
local_num_chunks_after (`int`, *optional*, defaults to 0):
|
338_9_12
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
local_num_chunks_after (`int`, *optional*, defaults to 0):
Number of following neighbouring chunks to attend to in `LocalSelfAttention` layer in addition to itself.
local_attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities in `LocalSelfAttention`.
lsh_attn_chunk_length (`int`, *optional*, defaults to 64):
Length of chunk which attends to itself in `LSHSelfAttention`. Chunking reduces memory complexity from
|
338_9_13
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Length of chunk which attends to itself in `LSHSelfAttention`. Chunking reduces memory complexity from
sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk
length (chunked self attention).
lsh_num_chunks_before (`int`, *optional*, defaults to 1):
Number of previous neighbouring chunks to attend to in `LSHSelfAttention` layer to itself.
lsh_num_chunks_after (`int`, *optional*, defaults to 0):
|
338_9_14
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
lsh_num_chunks_after (`int`, *optional*, defaults to 0):
Number of following neighbouring chunks to attend to in `LSHSelfAttention` layer to itself.
lsh_attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities in `LSHSelfAttention`.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
|
338_9_15
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
just in case (e.g., 512 or 1024 or 2048).
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_buckets (`int` or `List[int]`, *optional*):
Number of buckets, the key query vectors can be "hashed into" using the locality sensitive hashing scheme.
Each query key vector is hashed into a hash in `1, ..., num_buckets`. The number of buckets can also be
|
338_9_16
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Each query key vector is hashed into a hash in `1, ..., num_buckets`. The number of buckets can also be
factorized into a list for improved memory complexity. In this case, each query key vector is hashed into a
hash in `1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if `num_buckets` is
factorized into two factors. The number of buckets (or the product the factors) should approximately equal
|
338_9_17
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
factorized into two factors. The number of buckets (or the product the factors) should approximately equal
sequence length / lsh_chunk_length. If `num_buckets` not set, a good value is calculated on the fly.
num_hashes (`int`, *optional*, defaults to 1):
Number of hashing rounds (e.g., number of random rotations) in Local Sensitive Hashing scheme. The higher
`num_hashes`, the more accurate the `LSHSelfAttention` becomes, but also the more memory and time intensive
the hashing becomes.
|
338_9_18
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
the hashing becomes.
pad_token_id (`int`, *optional*, defaults to 0):
The token id for the padding token.
vocab_size (`int`, *optional*, defaults to 320):\
Vocabulary size of the Reformer model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`ReformerModel`].
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie input and output embeddings.
use_cache (`bool`, *optional*, defaults to `True`):
|
338_9_19
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
Whether to tie input and output embeddings.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Examples:
```python
>>> from transformers import ReformerConfig, ReformerModel
|
338_9_20
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig
|
.md
|
>>> # Initializing a Reformer configuration
>>> configuration = ReformerConfig()
>>> # Initializing a Reformer model (with random weights)
>>> model = ReformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
|
338_9_21
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer
|
.md
|
Construct a Reformer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece) .
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
|
338_10_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer
|
.md
|
contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
|
338_10_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer
|
.md
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
additional_special_tokens (`List[str]`, *optional*, defaults to `[]`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
|
338_10_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer
|
.md
|
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
|
338_10_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer
|
.md
|
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Methods: save_vocabulary
|
338_10_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizerfast
|
.md
|
Construct a "fast" Reformer tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
|
338_11_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizerfast
|
.md
|
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
|
338_11_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizerfast
|
.md
|
The token used is the `sep_token`.
</Tip>
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
additional_special_tokens (`List[str]`, *optional*):
Additional special tokens used by the tokenizer.
|
338_11_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodel
|
.md
|
The bare Reformer Model transformer outputting raw hidden-stateswithout any specific head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
|
338_12_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodel
|
.md
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
|
338_12_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodel
|
.md
|
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
|
338_12_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodelwithlmhead
|
.md
|
Reformer Model with a `language modeling` head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
|
338_13_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodelwithlmhead
|
.md
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
|
338_13_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodelwithlmhead
|
.md
|
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
|
338_13_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerformaskedlm
|
.md
|
Reformer Model with a `language modeling` head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
|
338_14_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerformaskedlm
|
.md
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
|
338_14_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerformaskedlm
|
.md
|
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
|
338_14_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforsequenceclassification
|
.md
|
Reformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
338_15_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforsequenceclassification
|
.md
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
|
338_15_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforsequenceclassification
|
.md
|
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
|
338_15_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforquestionanswering
|
.md
|
Reformer Model with a span classification head on top for extractive question-answering tasks like SQuAD / TriviaQA
( a linear layer on top of hidden-states output to compute `span start logits` and `span end logits`.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
338_16_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforquestionanswering
|
.md
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
|
338_16_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md
|
https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforquestionanswering
|
.md
|
and behavior.
Parameters:
config ([`ReformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
|
338_16_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/
|
.md
|
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
339_0_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/
|
.md
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
|
339_0_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview
|
.md
|
The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by
[Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Villemonte de la
Clergerie, [Djamé Seddah](https://huggingface.co/Djame), and [Benoît Sagot](https://huggingface.co/sagot). It is based on Facebook's RoBERTa model released in 2019. It is a model
|
339_1_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview
|
.md
|
trained on 138GB of French text.
The abstract from the paper is the following:
*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
models have either been trained on English data or on the concatenation of data in multiple languages. This makes
practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
|
339_1_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview
|
.md
|
practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
|
339_1_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview
|
.md
|
dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
downstream applications for French NLP.*
This model was contributed by [the ALMAnaCH team (Inria)](https://huggingface.co/almanach). The original code can be found [here](https://camembert-model.fr/).
<Tip>
|
339_1_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview
|
.md
|
<Tip>
This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well
as the information relative to the inputs and outputs.
</Tip>
|
339_1_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#resources
|
.md
|
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
|
339_2_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
This is the configuration class to store the configuration of a [`CamembertModel`] or a [`TFCamembertModel`]. It is
used to instantiate a Camembert model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Camembert
[almanach/camembert-base](https://huggingface.co/almanach/camembert-base) architecture.
|
339_3_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
[almanach/camembert-base](https://huggingface.co/almanach/camembert-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`CamembertModel`] or [`TFCamembertModel`].
|
339_3_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
`inputs_ids` passed when calling [`CamembertModel`] or [`TFCamembertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
|
339_3_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
|
339_3_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
339_3_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`CamembertModel`] or [`TFCamembertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
339_3_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
|
339_3_6
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
is_decoder (`bool`, *optional*, defaults to `False`):
|
339_3_7
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Example:
```python
>>> from transformers import CamembertConfig, CamembertModel
|
339_3_8
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig
|
.md
|
>>> # Initializing a Camembert almanach/camembert-base style configuration
>>> configuration = CamembertConfig()
>>> # Initializing a model (with random weights) from the almanach/camembert-base style configuration
>>> model = CamembertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
|
339_3_9
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
|
339_4_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
|
339_4_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
339_4_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
339_4_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
|
339_4_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED', '<unk>NOTUSED']`):
|
339_4_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
additional_special_tokens (`List[str]`, *optional*, defaults to `['<s>NOTUSED', '</s>NOTUSED', '<unk>NOTUSED']`):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
|
339_4_6
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
|
339_4_7
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer
|
.md
|
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
|
339_4_8
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
Construct a "fast" CamemBERT tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
|
339_5_0
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
|
339_5_1
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
|
339_5_2
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
|
339_5_3
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
|
339_5_4
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
|
339_5_5
|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md
|
https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast
|
.md
|
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
<frameworkcontent>
<pt>
|
339_5_6
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.