rrrr66254
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Glossa-BART

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 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+language:
+- en
+metrics:
+- bertscore
+base_model:
+- facebook/bart-base
 ---
 # Model Card for Model ID
+This model is a fine-tuned version of `facebook/bart-base`, trained to convert American Sign Language (ASL) gloss sequences into fluent English sentences. It is designed to assist in research, education, and accessibility applications involving gloss-based ASL interpretation.
+The model was trained using high-quality aligned pairs of gloss annotations and English translations, and evaluated using BERTScore.
 ## Model Details
 ### Model Description
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Dongjun Kim
+- **Model type:** Text2Text Generation
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [More Information Needed]
+## Intended Uses
+This model is fine-tuned for translating American Sign Language (ASL) gloss input sequences into natural, grammatically correct English sentences. It can be used for:
+- Building real-time sign language interpretation systems
+- Research in sign language understanding and low-resource language translation
+- Educational tools for ASL learners to see gloss-to-English transformation
+- Data augmentation for multimodal ASL translation tasks
+## Out-of-Scope Uses
+The model is **not** suitable for:
+- Translating from ASL videos or images directly (no visual input is processed)
+- Formal legal or medical translation without human validation
+- General-purpose translation outside ASL gloss context
+- Languages other than English
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("your-username/bart-gloss-finetuned")
+model = AutoModelForSeq2SeqLM.from_pretrained("your-username/bart-gloss-finetuned")
+gloss_input = "YOU GO STORE TOMORROW?"
+inputs = tokenizer(gloss_input, return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
 ## Bias, Risks, and Limitations
+This model is trained on American Sign Language (ASL) glosses mapped to natural English sentences. As such, it may inherit several limitations:
+- **Data bias**: If the training data overrepresents certain sentence structures, cultural expressions, or gloss forms, the model may produce outputs that lack variety or inclusivity.
+- **Limited linguistic scope**: The model only understands **ASL gloss** as input and **English** as output. It does not cover other sign languages or spoken/written languages.
+- **Context loss**: ASL gloss does not encode facial expressions, spatial grammar, or non-manual signals, which are essential in ASL. The model may misrepresent meaning as a result.
+- **Generalization risk**: The model may not generalize well to gloss styles or sentence structures it wasn’t trained on.
+Outputs should not be used in **critical settings** (e.g., legal, medical, or emergency interpreting) without human review.
+### Recommendations
+- Human-in-the-loop: Always have a fluent signer or linguist verify model outputs in any production or educational setting.
+- Data expansion: Consider fine-tuning with more diverse gloss datasets that include different dialects or informal structures.
+- Downstream use: If used as part of a larger translation or accessibility pipeline, include disclaimers about potential misinterpretation due to a lack of non-manual signals.
 ## Training Details
 ### Training Data
+The model was fine-tuned on a custom dataset of 1:1 pairs of ASL gloss and fluent English sentences.
+The glosses are structured representations of ASL without punctuation, articles, or verb conjugation. Each gloss sentence is paired with a corresponding English sentence that captures its intended meaning.
+The dataset was cleaned to remove non-English outputs, duplicates, and ill-formed pairs using custom filters.
 ### Training Procedure
+The training used the Hugging Face `Trainer` API with a sequence-to-sequence objective.
+The training leveraged a BART-based architecture (facebook/bart-base) to learn a mapping from gloss to fluent English sentences.
 #### Preprocessing [optional]
+- Input text was trimmed and normalized
+- Tokenizer: Pretrained BART tokenizer
+- Special tokens: `[INST]` and `[/INST]` were used to delimit gloss input and output reference
+#### Training Hyperparameters
+#### Training Hyperparameters
+- **Base model**: `facebook/bart-base`
+- **Epochs**: 3
+- **Learning rate**: 5e-5
+- **Batch size**: 4 per device (both train and eval)
+- **Gradient accumulation**: Not used
+- **Weight decay**: 0.01
+- **Learning rate scheduler**: Linear (default in Trainer)
+- **Precision**: Mixed precision (fp16=True)
+- **Evaluation strategy**: Per epoch
+- **Save strategy**: Per epoch (with `save_total_limit=2`)
+- **Logging frequency**: Every 50 steps
+- **Early stopping**: Custom callback based on BERTScore with patience = 2
+- **Evaluation metric**: BERTScore (F1), computed with `microsoft/deberta-xlarge-mnli`
 #### Factors
+This model does not explicitly disaggregate results by demographic group, signer identity, or domain. However, the training data may implicitly reflect distributional biases present in publicly available gloss datasets.
 #### Metrics
+- **Primary metric**: BERTScore (F1)
+- **Model selection**: Best checkpoint based on highest validation BERTScore-F1
+- BERTScore is preferred for this task due to its alignment with semantic quality over token-level exactness (e.g., BLEU or ROUGE)
 ### Results
+After 2 epochs of training, the model achieved:
+- **BERTScore-F1**: 0.83 on held-out evaluation set of 500 gloss-reference pairs
+- Qualitative inspection confirms that most outputs are fluent and contextually aligned, though some suffer from missing function words or incorrect verb tenses.
+#### Summary
+This model demonstrates strong potential for gloss-to-English translation, with near-human fluency in many cases. However, further work is needed to improve generalization to informal gloss styles and integrate non-manual features.
+## Model Card Authors
+- Dongjun Kim
 ## Model Card Contact
+- rrrr66254@gmail.com