Update README with comprehensive NER model documentation

README.md

@@ -1,199 +1,217 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- bert
+- ner
+- token-classification
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+datasets:
+- wikiann
+language:
+- en
+model-index:
+- name: bert-finetuned-ner-accelerate
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      type: wikiann
+      name: WikiANN English
+      config: en
+      split: validation
+    metrics:
+    - type: precision
+      value: 0.8092
+      name: Precision
+    - type: recall
+      value: 0.8407
+      name: Recall
+    - type: f1
+      value: 0.8247
+      name: F1
+    - type: accuracy
+      value: 0.9250
+      name: Accuracy
 ---
 
-# Model Card for Model ID
+# BERT Fine-tuned for Named Entity Recognition
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the WikiANN (WikiNER) English dataset for Named Entity Recognition (NER). The model can identify and classify named entities in text into predefined categories.
 
+### Performance Metrics
 
-## Model Details
+The model achieves strong performance on the WikiANN English validation set:
+- **F1 Score**: 82.47%
+- **Precision**: 80.92%
+- **Recall**: 84.07%
+- **Accuracy**: 92.50%
 
-### Model Description
+## Supported Entity Types
 
-<!-- Provide a longer summary of what this model is. -->
+The model recognizes 3 main entity types with BIO tagging:
+- **PER** (Person): Names of people
+- **ORG** (Organization): Names of organizations, companies, institutions
+- **LOC** (Location): Names of locations, cities, countries
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Label Set
+- `O`: Outside of any entity
+- `B-PER`: Beginning of a person entity
+- `I-PER`: Inside a person entity
+- `B-ORG`: Beginning of an organization entity
+- `I-ORG`: Inside an organization entity
+- `B-LOC`: Beginning of a location entity
+- `I-LOC`: Inside a location entity
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## How to Use
 
-### Model Sources [optional]
+### Using with Transformers Pipeline
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import pipeline
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Load the NER pipeline
+nlp = pipeline("ner", model="yiwenX/bert-finetuned-ner-accelerate")
 
-## Uses
+# Example text
+text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Get predictions
+results = nlp(text)
+print(results)
+```
 
-### Direct Use
+### Using with AutoModel
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
 
-[More Information Needed]
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("yiwenX/bert-finetuned-ner-accelerate")
+model = AutoModelForTokenClassification.from_pretrained("yiwenX/bert-finetuned-ner-accelerate")
 
-### Downstream Use [optional]
+# Example text
+text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
 
-[More Information Needed]
+# Get predictions
+predictions = torch.argmax(outputs.logits, dim=-1)
+tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
 
-### Out-of-Scope Use
+# Map predictions to labels
+label_list = model.config.id2label
+entities = []
+for token, pred in zip(tokens, predictions[0]):
+    if pred != 0:  # 0 is 'O' (outside)
+        entities.append((token, label_list[pred.item()]))
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+print(entities)
+```
 
-[More Information Needed]
+## Intended Uses & Limitations
 
-## Bias, Risks, and Limitations
+### Intended Uses
+- Extract named entities from English text
+- Information extraction tasks
+- Text preprocessing for downstream NLP applications
+- Content analysis and categorization
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+### Limitations
+- The model is trained on WikiANN dataset, which may not generalize well to domain-specific texts
+- Performance may vary on informal text (social media, chat messages)
+- Limited to English language only
+- Only recognizes PER, ORG, and LOC entity types
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+The model was trained on the **WikiANN (WikiNER) English dataset**, which contains:
+- **Training samples**: 20,000
+- **Validation samples**: 10,000
+- **Test samples**: 10,000
 
-[More Information Needed]
+WikiANN is a multilingual named entity recognition dataset derived from Wikipedia, providing annotated text with named entity labels.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+The following hyperparameters were used during training:
+- **Learning rate**: 2e-05
+- **Batch size**: 16 (both training and evaluation)
+- **Number of epochs**: 3
+- **Optimizer**: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
+- **LR scheduler**: Linear
+- **Seed**: 42
 
-#### Speeds, Sizes, Times [optional]
+#### Training Results
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+| Epoch | Training Loss | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-----:|:-------------:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.0   | 0.3066        | 0.2636          | 78.23%    | 81.86% | 80.00% | 91.89%   |
+| 2.0   | 0.2059        | 0.2566          | 79.60%    | 83.37% | 81.44% | 92.42%   |
+| 3.0   | 0.1455        | 0.2777          | 80.92%    | 84.07% | 82.47% | 92.50%   |
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+### Testing Data
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+The model was evaluated on the WikiANN English validation set (10,000 samples).
 
-**BibTeX:**
+### Metrics
 
-[More Information Needed]
+The model is evaluated using standard NER metrics:
+- **Precision**: The percentage of predicted entities that are correct
+- **Recall**: The percentage of actual entities that were correctly identified
+- **F1 Score**: The harmonic mean of precision and recall
+- **Accuracy**: Token-level classification accuracy
 
-**APA:**
+### Results Summary
 
-[More Information Needed]
+Final model performance (Epoch 3):
+- **F1 Score**: 82.47%
+- **Precision**: 80.92%
+- **Recall**: 84.07%
+- **Accuracy**: 92.50%
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-[More Information Needed]
+## Framework Versions
 
-## More Information [optional]
+- **Transformers**: 4.56.0
+- **PyTorch**: 2.8.0+cu128
+- **Datasets**: 4.0.0
+- **Tokenizers**: 0.22.0
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model in your research, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{bert-finetuned-ner-accelerate,
+  author = {yiwenX},
+  title = {BERT Fine-tuned for Named Entity Recognition},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/yiwenX/bert-finetuned-ner-accelerate}
+}
+```
 
-## Model Card Contact
+## License
 
-[More Information Needed]
+This model is licensed under the Apache 2.0 License.