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bert-large-uncased-wnut_17-full

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bert-large-uncased-wnut_17-full
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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- trl
- sft
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-uncased-wnut_17-full
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.6546310832025117
    - name: Recall
      type: recall
      value: 0.386468952734013
    - name: F1
      type: f1
      value: 0.486013986013986
    - name: Accuracy
      type: accuracy
      value: 0.9493394895472618
---

<!-- 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-large-uncased-wnut_17-full

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4040
- Precision: 0.6546
- Recall: 0.3865
- F1: 0.4860
- Accuracy: 0.9493

## 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: 5e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.2471          | 0.6341    | 0.3726 | 0.4694 | 0.9461   |
| No log        | 2.0   | 426  | 0.2454          | 0.5882    | 0.3707 | 0.4548 | 0.9475   |
| 0.1196        | 3.0   | 639  | 0.3091          | 0.6278    | 0.3689 | 0.4647 | 0.9490   |
| 0.1196        | 4.0   | 852  | 0.3758          | 0.6536    | 0.3411 | 0.4482 | 0.9473   |
| 0.0235        | 5.0   | 1065 | 0.3127          | 0.5632    | 0.4004 | 0.4680 | 0.9490   |
| 0.0235        | 6.0   | 1278 | 0.3988          | 0.6562    | 0.3698 | 0.4730 | 0.9492   |
| 0.0235        | 7.0   | 1491 | 0.4040          | 0.6546    | 0.3865 | 0.4860 | 0.9493   |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1