| --- |
| license: apache-2.0 |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - wnut_17 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: wnut-distilbert-finetuned |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: wnut_17 |
| type: wnut_17 |
| config: wnut_17 |
| split: test |
| args: wnut_17 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.533625730994152 |
| - name: Recall |
| type: recall |
| value: 0.3382761816496756 |
| - name: F1 |
| type: f1 |
| value: 0.414066931366988 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9443803172160232 |
| language: |
| - en |
| library_name: adapter-transformers |
| pipeline_tag: token-classification |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> |
|
|
| # wnut-distilbert-finetuned |
|
|
| This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the WNUT 2017 dataset for Named Entity Recognition (NER). |
|
|
| ## Model Description |
|
|
| The `wnut-distilbert-finetuned` model is designed for token classification tasks, specifically for Named Entity Recognition (NER). It leverages the DistilBERT architecture, which is a smaller, faster version of BERT with reduced computational requirements, while maintaining competitive performance. |
|
|
| ## Intended Uses & Limitations |
|
|
| ### Intended Uses |
| - **Named Entity Recognition (NER)**: Extract and classify entities such as names, locations, organizations, etc., from text. |
| - **Text Analysis**: Enhance applications in information extraction, question answering, and text understanding. |
|
|
| ### How to Use |
| To use this model, you can load it using the Hugging Face Transformers library. Below is an example of how to perform inference using the model: |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForTokenClassification |
| from transformers import pipeline |
| |
| # Load the tokenizer and model |
| tokenizer = AutoTokenizer.from_pretrained("Ashaduzzaman/wnut-distilbert-finetuned") |
| model = AutoModelForTokenClassification.from_pretrained("Ashaduzzaman/bert-finetuned-ner") |
| |
| # Create a pipeline for NER |
| ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer) |
| |
| # Example inference |
| text = "Hugging Face Inc. is based in New York City." |
| entities = ner_pipeline(text) |
| |
| print(entities) |
| ``` |
| ### Limitations |
| - **Performance on Other Domains**: Performance may vary when applied to domains or data types different from the WNUT 2017 dataset. |
| - **Entity Types**: The model is trained on the specific entity types present in the WNUT 2017 dataset and may not perform well on entity types not covered by the training data. |
| - **Data Sensitivity**: The model may have biases or limitations based on the training data it was exposed to. |
|
|
| ## Training and Evaluation Data |
|
|
| ### Training Data |
| - **Dataset**: WNUT 2017, which includes a set of texts annotated with entities relevant to the dataset. |
| - **Data Split**: Training and validation splits of the WNUT 2017 dataset were used during the fine-tuning process. |
|
|
| ### Evaluation Data |
| - **Dataset**: WNUT 2017 test set, used to evaluate model performance after fine-tuning. |
|
|
| ## Training Procedure |
|
|
| ### Training Hyperparameters |
| - **Learning Rate**: 2e-05 |
| - **Train Batch Size**: 16 |
| - **Eval Batch Size**: 16 |
| - **Seed**: 42 |
| - **Optimizer**: Adam with betas=(0.9, 0.999) and epsilon=1e-08 |
| - **Learning Rate Scheduler**: Linear |
| - **Number of Epochs**: 3 |
|
|
| ### Training Results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 213 | 0.2751 | 0.5114 | 0.2289 | 0.3163 | 0.9385 | |
| | No log | 2.0 | 426 | 0.2627 | 0.5398 | 0.3327 | 0.4117 | 0.9434 | |
| | 0.1832 | 3.0 | 639 | 0.2704 | 0.5336 | 0.3383 | 0.4141 | 0.9444 | |
|
|
| ### Framework Versions |
| - **Transformers**: 4.42.4 |
| - **Pytorch**: 2.3.1+cu121 |
| - **Datasets**: 2.21.0 |
| - **Tokenizers**: 0.19.1 |
