---
library_name: transformers
license: mit
base_model: microsoft/prophetnet-large-uncased-cnndm
datasets:
- eilamc14/wikilarge-clean
language:
- en
tags:
- prophetnet
- text-simplification
- WikiLarge
model-index:
- name: prophetnet-large-uncased-cnndm-text-simplification
  results:
  - task:
      type: text2text-generation
      name: Text Simplification
    dataset:
      name: ASSET
      type: facebook/asset
      url: https://huggingface.co/datasets/facebook/asset
      split: test
    metrics:
    - type: SARI
      value: 38.01
    - type: FKGL
      value: 7.70
    - type: BERTScore
      value: 67.82
    - type: LENS
      value: 60.85
    - type: Identical ratio (ci)
      value: 0.11

  - task:
      type: text2text-generation
      name: Text Simplification
    dataset:
      name: MEDEASI
      type: cbasu/Med-EASi
      url: https://huggingface.co/datasets/cbasu/Med-EASi
      split: test
    metrics:
    - type: SARI
      value: 36.86
    - type: FKGL
      value: 8.50
    - type: BERTScore
      value: 38.31
    - type: LENS
      value: 51.36
    - type: Identical ratio (ci)
      value: 0.04

  - task:
      type: text2text-generation
      name: Text Simplification
    dataset:
      name: OneStopEnglish
      type: OneStopEnglish
      url: https://github.com/nishkalavallabhi/OneStopEnglishCorpus
      split: advanced→elementary
    metrics:
    - type: SARI
      value: 39.17
    - type: FKGL
      value: 7.00
    - type: BERTScore
      value: 65.22
    - type: LENS
      value: 61.53
    - type: Identical ratio (ci)
      value: 0.13
---

# Model Card for Model ID

This is one of the models fine-tuned on text simplification for [Simplify This](https://github.com/eilamc14/Simplify-This) project.

## Model Details

### Model Description

Fine-tuned **sequence-to-sequence (encoder–decoder) Transformer** for **English text simplification**.  
Trained on the dataset **`eilamc14/wikilarge-clean`** (cleaned WikiLarge-style pairs).

- **Model type:** Seq2Seq Transformer (encoder–decoder)
- **Language (NLP):** English
- **License:** `mit`
- **Finetuned from model:** `microsoft/prophetnet-large-uncased-cnndm`

### Model Sources

- **Repository (code):** https://github.com/eilamc14/Simplify-This
- **Dataset:** https://huggingface.co/datasets/eilamc14/wikilarge-clean
- **Paper [optional]:** —
- **Demo [optional]:** —

## Uses

### Direct Use

The model is intended for **English text simplification**.

- **Input format:** `Simplify: <complex sentence>`
- **Output:** `<simplified sentence>`

**Typical uses**
- Research on automatic text simplification
- Benchmarking against other simplification systems
- Demos/prototypes that require simpler English rewrites

### Downstream Use

This repository already contains a **fine-tuned** model specialized for text simplification.

Further fine-tuning is **optional** and mainly relevant when:
- Adapting to a markedly different domain (e.g., medical/legal/news)
- Addressing specific failure modes (e.g., over/under-simplification, factual drops)
- Distilling/quantizing for deployment constraints

When fine-tuning further, keep the same input convention: `Simplify: <...>`.

### Out-of-Scope Use

Not intended for:
- Tasks unrelated to simplification (dialogue, translation etc.)
- Production use without additional safety filtering (no toxicity/bias mitigation)
- Languages other than English
- High-stakes settings (legal/medical advice, safety-critical decisions)


## Bias, Risks, and Limitations

The model was trained on **Wikipedia and Simple English Wikipedia** alignments (via WikiLarge).  
As a result, it inherits the characteristics and limitations of this data:

- **Domain bias:** Simplifications may reflect encyclopedic style; performance may degrade on informal, technical, or domain-specific text (e.g., medical/legal/news).
- **Content bias:** Wikipedia content itself contains biases in coverage, cultural perspective, and phrasing. Simplified outputs may reflect or amplify these.
- **Simplification quality:** The model may:
  - Over-simplify (drop important details)
  - Under-simplify (retain complex phrasing)
  - Produce ungrammatical or awkward rephrasings
- **Language limitation:** Only suitable for English. Applying to other languages is unsupported.
- **Safety limitation:** The model has not been aligned to avoid toxic, biased, or harmful content. If the input text contains such content, the output may reproduce or modify it without safeguards.


### Recommendations

- **Evaluation required:** Always evaluate the model in the target domain before deployment. Benchmark simplification quality (e.g., with SARI, FKGL, BERTScore, LENS, human evaluation).
- **Human oversight:** Use human-in-the-loop review for applications where meaning preservation is critical (education, accessibility tools, etc.).
- **Attribution:** Preserve source attribution where required (Wikipedia → CC BY-SA).
- **Not for high-stakes use:** Avoid legal, medical, or safety-critical applications without extensive validation and domain adaptation.

## How to Get Started with the Model

Load the model and tokenizer directly from the Hugging Face Hub:

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "eilamc14/bart-base-text-simplification"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

# Example input
PREFIX = "Simplify: "
text = "The committee deemed the proposal unnecessarily complicated."

# Tokenize and generate
inputs = tokenizer(PREFIX+text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64, num_beams=4)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data

[WikiLarge-clean](https://huggingface.co/datasets/eilamc14/wikilarge-clean) Dataset

### Training Procedure

- **Hardware:** NVIDIA L4 GPU on Google Colab
- **Objective:** Standard sequence-to-sequence cross-entropy loss
- **Training type:** Full fine-tuning of all parameters (no LoRA/PEFT used)
- **Batching:** Dynamic padding with Hugging Face `Trainer` / PyTorch DataLoader
- **Evaluation:** Monitored on the `validation` split with metrics (SARI and identical_ratio)
- **Stopping criteria:** Early stopping CallBack based on validation performance
  
#### Preprocessing

The dataset was preprocessed by prefixing each source sentence with **"Simplify: "** and tokenizing both the source (inputs) and target (labels).

#### Memory & Checkpointing

To reduce VRAM during training, gradient checkpointing was enabled and the KV cache was disabled:

```python
model.config.use_cache = False          # required when using gradient checkpointing
model.gradient_checkpointing_enable()   # saves memory at the cost of extra compute
```

**Notes**
- Disabling `use_cache` avoids warnings/conflicts with gradient checkpointing and reduces memory usage in the forward pass.
- Gradient checkpointing trades **GPU memory ↓** for **training speed ↓** (extra recomputation).
- For **inference/evaluation**, re-enable the cache for faster generation:

```python
model.config.use_cache = True
```

#### Training Hyperparameters

The models were trained with Hugging Face `Seq2SeqTrainingArguments`.  
Hyperparameters varied slightly across models and runs to optimize, and full logs (batch size, steps, exact LR schedule) were not preserved.  
Below are the **typical defaults** used:

- **Epochs:** 5  
- **Evaluation strategy:** every 300 steps  
- **Save strategy:** every 300 steps (keep best model, `eval_loss` as criterion)  
- **Learning rate:** ~3e-5  
- **Batch size:** ~8-64 , depends on model size
- **Optimizer:** `adamw_torch_fused`
- **Precision:** bf16 
- **Generation config (during eval):** `max_length=128`, `num_beams=4`, `predict_with_generate=True`  
- **Other settings:**  
  - Weight decay: 0.01  
  - Label smoothing: 0.1  
  - Warmup ratio: 0.1  
  - Max grad norm: 0.5  
  - Dataloader workers: 8 (L4 GPU)  

> Because hyperparameters were adjusted between runs and not all were logged, exact reproduction may differ slightly.

## Evaluation

### Testing Data

- [**ASSET**](https://huggingface.co/datasets/facebook/asset) (test subset)
- [**MEDEASI**](https://huggingface.co/datasets/cbasu/Med-EASi) (test subset)
- [**OneStopEnglish**](https://github.com/nishkalavallabhi/OneStopEnglishCorpus) (advanced → elementary)

### Metrics

- **Identical ratio (ci)** — share of outputs identical to the source (case insensitive), both normalized by basic, language-agnostic: strip, NFKC, collapse spaces
- **SARI** — main simplification metric (higher is better)
- **FKGL** — readability grade level (lower is simpler)
- **BERTScore (F1)** — semantic similarity (higher is better)
- **LENS** — composite simplification quality score (higher is better)
  
### Generation Arguments

```python
gen_args = dict(
    max_new_tokens=64,
    num_beams=4,
    length_penalty=1.0,
    no_repeat_ngram_size=3,
    early_stopping=True,
    do_sample=False,
)
```

### Results

| Dataset            | Identical ratio (ci) |  SARI | FKGL | BERTScore |  LENS |
|--------------------|---------------------:|------:|-----:|----------:|------:|
| **ASSET**          |                 0.11 | 38.01 | 7.70 |     67.82 | 60.85 |
| **MEDEASI**        |                 0.04 | 36.86 | 8.50 |     38.31 | 51.36 |
| **OneStopEnglish** |                 0.13 | 39.17 | 7.00 |     65.22 | 61.53 |


## Environmental Impact

- **Hardware Type:** Single NVIDIA L4 GPU (Google Colab)
- **Hours used:** Approx. 5–10
- **Cloud Provider:** Google Cloud (via Colab)
- **Compute Region:** Unknown (Google Colab dynamic allocation)
- **Carbon Emitted:** Estimated to be very low (< a few kg CO₂eq), since training was limited to a single GPU for a small number of hours.

## Citation

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]