Update README.md

README.md

@@ -35,47 +35,74 @@ model-index:
     - 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. -->
+<!-- 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_17 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2704
-- Precision: 0.5336
-- Recall: 0.3383
-- F1: 0.4141
-- Accuracy: 0.9444
+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
+## Model Description
 
-More information needed
+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 & Limitations
 
-More information needed
+### 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.
 
-## Training and evaluation data
+### 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:
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-## Training procedure
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Ashaduzzaman/wnut-distilbert-finetuned")
+model = AutoModelForTokenClassification.from_pretrained("Ashaduzzaman/bert-finetuned-ner")
 
-### Training hyperparameters
+# Create a pipeline for NER
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
 
-The following hyperparameters were used during training:
-- 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
-- lr_scheduler_type: linear
-- num_epochs: 3
+# Example inference
+text = "Hugging Face Inc. is based in New York City."
+entities = ner_pipeline(text)
 
-### Training results
+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 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
@@ -83,10 +110,8 @@ The following hyperparameters were used during training:
 | 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
+### Framework Versions
+- **Transformers**: 4.42.4
+- **Pytorch**: 2.3.1+cu121
+- **Datasets**: 2.21.0
+- **Tokenizers**: 0.19.1