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bert-base-uncased-finetuned-ner

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bert-base-uncased-finetuned-ner
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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.942891335567257
    - name: Recall
      type: recall
      value: 0.9658232813572619
    - name: F1
      type: f1
      value: 0.9542195523689565
    - name: Accuracy
      type: accuracy
      value: 0.9763595874355369
---

<!-- 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-base-uncased-finetuned-ner

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1204
- Precision: 0.9429
- Recall: 0.9658
- F1: 0.9542
- Accuracy: 0.9764

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1823        | 1.0   | 1224  | 0.1059          | 0.9301    | 0.9628 | 0.9462 | 0.9731   |
| 0.1142        | 2.0   | 2448  | 0.1163          | 0.9203    | 0.9717 | 0.9453 | 0.9698   |
| 0.0812        | 3.0   | 3672  | 0.1000          | 0.9427    | 0.9705 | 0.9564 | 0.9773   |
| 0.0603        | 4.0   | 4896  | 0.0970          | 0.9424    | 0.9717 | 0.9568 | 0.9773   |
| 0.0516        | 5.0   | 6120  | 0.1018          | 0.9416    | 0.9720 | 0.9566 | 0.9772   |
| 0.0418        | 6.0   | 7344  | 0.1044          | 0.9446    | 0.9704 | 0.9574 | 0.9778   |
| 0.0361        | 7.0   | 8568  | 0.1070          | 0.9422    | 0.9725 | 0.9571 | 0.9775   |
| 0.0296        | 8.0   | 9792  | 0.1166          | 0.9438    | 0.9708 | 0.9571 | 0.9776   |
| 0.0242        | 9.0   | 11016 | 0.1174          | 0.9437    | 0.9671 | 0.9553 | 0.9767   |
| 0.0231        | 10.0  | 12240 | 0.1204          | 0.9429    | 0.9658 | 0.9542 | 0.9764   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0