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copilot_wnut_model

Token Classification
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PyTorch
distilbert
Generated from Trainer
Eval Results
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copilot_wnut_model
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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: copilot_wnut_model
  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: 0.5803921568627451
    - name: Recall
      type: recall
      value: 0.4114921223354958
    - name: F1
      type: f1
      value: 0.48156182212581344
    - name: Accuracy
      type: accuracy
      value: 0.9483562053781369
---

<!-- 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. -->

# copilot_wnut_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3448
- Precision: 0.5804
- Recall: 0.4115
- F1: 0.4816
- Accuracy: 0.9484

## 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: 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  | 0.2743          | 0.6364    | 0.2725 | 0.3816 | 0.9401   |
| No log        | 2.0   | 426  | 0.2598          | 0.5977    | 0.3346 | 0.4290 | 0.9445   |
| 0.1759        | 3.0   | 639  | 0.3063          | 0.6741    | 0.3086 | 0.4234 | 0.9445   |
| 0.1759        | 4.0   | 852  | 0.3097          | 0.5930    | 0.3605 | 0.4484 | 0.9463   |
| 0.0477        | 5.0   | 1065 | 0.2962          | 0.5558    | 0.4106 | 0.4723 | 0.9474   |
| 0.0477        | 6.0   | 1278 | 0.3218          | 0.5792    | 0.3967 | 0.4708 | 0.9474   |
| 0.0477        | 7.0   | 1491 | 0.3199          | 0.5595    | 0.4096 | 0.4730 | 0.9477   |
| 0.022         | 8.0   | 1704 | 0.3385          | 0.5938    | 0.4106 | 0.4855 | 0.9481   |
| 0.022         | 9.0   | 1917 | 0.3311          | 0.5687    | 0.4217 | 0.4843 | 0.9478   |
| 0.0123        | 10.0  | 2130 | 0.3448          | 0.5804    | 0.4115 | 0.4816 | 0.9484   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1