Update README.md

README.md

@@ -39,16 +39,18 @@ model-index:
       type: accuracy
       value: 0.9863572143403779
 pipeline_tag: token-classification
-inference: true
+language:
+- en
 ---
 
 <!-- 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. -->
 
+
 # bert-finetuned-ner
 
 ## Model Description
-This model is a Named Entity Recognition (NER) model built using PyTorch and trained on the CoNLL-2003 dataset. The model is designed to identify and classify named entities in text into categories such as persons (PER), organizations (ORG), locations (LOC), and miscellaneous entities (MISC).
+This model is a Named Entity Recognition (NER) model built using PyTorch and fine-tuned on the CoNLL-2003 dataset. The model is designed to identify and classify named entities in text into categories such as persons (PER), organizations (ORG), locations (LOC), and miscellaneous entities (MISC).
 
 ## Intended Uses & Limitations
 **Intended Uses:**
@@ -61,7 +63,7 @@ This model is a Named Entity Recognition (NER) model built using PyTorch and tra
 - **Error Propagation:** Incorrect predictions may propagate to downstream tasks, affecting overall performance.
 
 ## How to Use
-To use this model, load it through the Hugging Face Transformers library. Below is a basic example:
+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
@@ -81,6 +83,11 @@ entities = ner_pipeline(text)
 print(entities)
 ```
 
+### Troubleshooting
+If the model isn't performing as expected, consider checking the following:
+- Ensure that the input text is in English, as the model was trained on English data.
+- Adjust the model's confidence threshold for entity detection to filter out less confident predictions.
+
 ## Limitations and Bias
 - **Bias in Data:** The model is trained on the CoNLL-2003 dataset, which may contain biases related to the sources of the text. The model might underperform on entities not well represented in the training data.
 - **Overfitting:** The model may overfit to the specific entities present in the CoNLL-2003 dataset, affecting its generalization to new entities or text styles.
@@ -89,26 +96,23 @@ print(entities)
 The model was trained on the CoNLL-2003 dataset, a widely used benchmark dataset for NER tasks. The dataset contains annotated text from news articles, with labels for persons, organizations, locations, and miscellaneous entities.
 
 ## Training Procedure
-The model was fine-tuned using a pre-trained transformer model (e.g., BERT) with a token classification head for NER. The training involved:
-- **Optimizer:** AdamW optimizer
-- **Learning Rate:** Learning rate scheduler was employed
-- **Batch Size:** Defined in the notebook based on available resources
-- **Epochs:** The model was trained for a specified number of epochs until convergence
-- **Evaluation:** Model performance was evaluated on a validation set, with metrics like F1-score, precision, and recall.
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
+The model was fine-tuned using the pre-trained BERT model (`bert-base-cased`) with a token classification head for NER. The training process involved:
+- **Optimizer:** AdamW optimizer with betas=(0.9, 0.999) and epsilon=1e-08
+- **Learning Rate:** A linear learning rate scheduler was employed starting from 2e-05
+- **Batch Size:** 8 for both training and evaluation
+- **Epochs:** The model was trained for 3 epochs
+- **Evaluation:** Model performance was evaluated on a validation set with metrics like F1-score, precision, recall, and accuracy.
+
+### Training Hyperparameters
+- **Learning Rate:** 2e-05
+- **Batch Size (train/eval):** 8/8
+- **Seed:** 42
+- **Optimizer:** Adam with betas=(0.9, 0.999) and epsilon=1e-08
+- **LR Scheduler Type:** Linear
+- **Number of Epochs:** 3
 
 ## Evaluation Results
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the CoNLL-2003 test set, with performance measured using standard NER metrics:
+This model was evaluated on the CoNLL-2003 test set, with performance measured using standard NER metrics:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
@@ -116,11 +120,9 @@ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/b
 | 0.0359        | 2.0   | 3512 | 0.0693          | 0.9265    | 0.9418 | 0.9341 | 0.9847   |
 | 0.0222        | 3.0   | 5268 | 0.0599          | 0.9347    | 0.9512 | 0.9429 | 0.9864   |
 
-These results indicate the model's ability to correctly identify and classify named entities in text.
-
-## 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
+!