Create README.md

README.md

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+---
+tags:
+- ner
+---
+
+# NER NER-finetuning-BERT
+This is the BERT-cased model for NER [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) using the CONLL2002 dataset. The results were as follows:
+
+- Precision: 0.8265
+- Recall: 0.8443
+- F1: 0.8353
+- Accuracy: 0.9786
+
+
+## Model description
+
+Fine-Tuned BERT-cased for Named Entity Recognition (NER)
+Overview:
+This model is a fine-tuned version of the bert-cased pre-trained model specifically tailored for the task of Named Entity Recognition (NER). BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art transformer-based model designed to understand the context of words in a sentence by considering both the left and right surrounding words. The bert-cased variant ensures that the model distinguishes between uppercase and lowercase letters, preserving the case sensitivity which is crucial for NER tasks.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - evaluation_strategy="epoch",
+- save_strategy="epoch",
+- learning_rate=2e-5,
+- num_train_epochs=4,
+- per_device_train_batch_size=16,
+- weight_decay=0.01,
+
+
+### Training results
+
+
+| Epoch | Training Loss | Validation Loss |
+|:-------:|:---------------:|:-----------------:|
+| 1       | 0.005700        | 0.258581          |
+| 2       | 0.004600        | 0.248794          |
+| 3       | 0.002800        | 0.257513          |
+| 4       | 0.002100        | 0.275097          |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1