change_idf (#1)
Browse files- Fix models to be up to date with the changes (73496a3c4d85eefe100806aa53cebff115a3c682)
- README.md +1 -1
- config.json +0 -19
- modules.json +1 -1
- query_0_IDF/special_tokens_map.json +0 -37
- {query_0_IDF → query_0_SparseStaticEmbedding}/config.json +2 -2
- {query_0_IDF → query_0_SparseStaticEmbedding}/model.safetensors +1 -1
- query_0_SparseStaticEmbedding/special_tokens_map.json +7 -0
- {query_0_IDF → query_0_SparseStaticEmbedding}/tokenizer.json +2 -14
- {query_0_IDF → query_0_SparseStaticEmbedding}/tokenizer_config.json +58 -65
- {query_0_IDF → query_0_SparseStaticEmbedding}/vocab.txt +0 -0
- router_config.json +20 -0
- train_script.py +14 -10
README.md
CHANGED
@@ -372,7 +372,7 @@ This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.ne
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```
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SparseEncoder(
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(0): Router(
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-
(
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(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
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(document_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
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)
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```
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SparseEncoder(
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(0): Router(
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+
(query_0_SparseStaticEmbedding): SparseStaticEmbedding ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
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(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
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(document_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
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)
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config.json
DELETED
@@ -1,19 +0,0 @@
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-
{
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"types": {
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"query_0_IDF": "sentence_transformers.sparse_encoder.models.IDF",
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"document_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer",
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"document_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling"
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},
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"structure": {
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"query": [
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"query_0_IDF"
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],
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"document": [
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"document_0_MLMTransformer",
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"document_1_SpladePooling"
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]
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},
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"parameters": {
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"allow_empty_key": true
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}
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}
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modules.json
CHANGED
@@ -3,6 +3,6 @@
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"idx": 0,
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"name": "0",
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"path": "",
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-
"type": "sentence_transformers.models.
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}
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]
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Router"
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}
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]
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query_0_IDF/special_tokens_map.json
DELETED
@@ -1,37 +0,0 @@
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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{query_0_IDF → query_0_SparseStaticEmbedding}/config.json
RENAMED
@@ -1,3 +1,3 @@
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{
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"frozen": false
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}
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{
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"frozen": false
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}
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{query_0_IDF → query_0_SparseStaticEmbedding}/model.safetensors
RENAMED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 122168
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:82ca927cba4916c595053947bfea48b28692538d894ab9f94cceca63f57a845a
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size 122168
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query_0_SparseStaticEmbedding/special_tokens_map.json
ADDED
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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{query_0_IDF → query_0_SparseStaticEmbedding}/tokenizer.json
RENAMED
@@ -1,19 +1,7 @@
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{
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"version": "1.0",
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"truncation":
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-
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"max_length": 512,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": {
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"strategy": "BatchLongest",
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"direction": "Right",
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"pad_to_multiple_of": null,
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"pad_id": 0,
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"pad_type_id": 0,
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"pad_token": "[PAD]"
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},
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"added_tokens": [
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{
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"id": 0,
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{
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"version": "1.0",
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"truncation": null,
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"padding": null,
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"added_tokens": [
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{
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"id": 0,
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{query_0_IDF → query_0_SparseStaticEmbedding}/tokenizer_config.json
RENAMED
@@ -1,65 +1,58 @@
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"
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"
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"
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"
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"
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"
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"
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"
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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{query_0_IDF → query_0_SparseStaticEmbedding}/vocab.txt
RENAMED
File without changes
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router_config.json
ADDED
@@ -0,0 +1,20 @@
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{
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"types": {
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"query_0_SparseStaticEmbedding": "sentence_transformers.sparse_encoder.models.SparseStaticEmbedding.SparseStaticEmbedding",
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"document_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer.MLMTransformer",
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"document_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling.SpladePooling"
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},
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"structure": {
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"query": [
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"query_0_SparseStaticEmbedding"
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],
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"document": [
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"document_0_MLMTransformer",
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"document_1_SpladePooling"
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]
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},
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"parameters": {
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"default_route": "query",
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"allow_empty_key": true
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}
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}
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train_script.py
CHANGED
@@ -8,8 +8,8 @@ from sentence_transformers import (
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from sentence_transformers.sparse_encoder.losses import SpladeLoss, SparseMultipleNegativesRankingLoss
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from sentence_transformers.training_args import BatchSamplers
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from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
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from sentence_transformers.sparse_encoder.models import SpladePooling, MLMTransformer,
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from sentence_transformers.models import
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import logging
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@@ -17,20 +17,24 @@ logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:
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# 1. Load a model to finetune with 2. (Optional) model card data
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mlm_transformer = MLMTransformer("prajjwal1/bert-tiny", tokenizer_args={"model_max_length": 512})
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splade_pooling = SpladePooling(
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model = SparseEncoder(
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modules=[
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model_card_data=SparseEncoderModelCardData(
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language="en",
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license="apache-2.0",
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model_name="Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples",
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)
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)
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# 3. Load a dataset to finetune on
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model.save_pretrained(f"models/{run_name}/final")
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# 10. (Optional) Push it to the Hugging Face Hub
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model.push_to_hub(run_name)
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from sentence_transformers.sparse_encoder.losses import SpladeLoss, SparseMultipleNegativesRankingLoss
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from sentence_transformers.training_args import BatchSamplers
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from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
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from sentence_transformers.sparse_encoder.models import SpladePooling, MLMTransformer, SparseStaticEmbedding
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from sentence_transformers.models import Router
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import logging
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# 1. Load a model to finetune with 2. (Optional) model card data
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mlm_transformer = MLMTransformer("prajjwal1/bert-tiny", tokenizer_args={"model_max_length": 512})
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splade_pooling = SpladePooling(
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pooling_strategy="max", word_embedding_dimension=mlm_transformer.get_sentence_embedding_dimension()
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)
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router = Router(
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{
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"query": [SparseStaticEmbedding(tokenizer=mlm_transformer.tokenizer, frozen=False)],
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"document": [mlm_transformer, splade_pooling],
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}
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)
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model = SparseEncoder(
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modules=[router],
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model_card_data=SparseEncoderModelCardData(
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language="en",
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license="apache-2.0",
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model_name="Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples",
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),
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)
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# 3. Load a dataset to finetune on
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model.save_pretrained(f"models/{run_name}/final")
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# 10. (Optional) Push it to the Hugging Face Hub
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model.push_to_hub(run_name)
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