transformers tensorboard safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# my_awesome_wnut_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0581
- Precision: 0.9128
- Recall: 0.9097
- F1: 0.9112
- Accuracy: 0.9802

## 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: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0802        | 1.0   | 3750  | 0.0687          | 0.8897    | 0.8952 | 0.8924 | 0.9760   |
| 0.0519        | 2.0   | 7500  | 0.0581          | 0.9128    | 0.9097 | 0.9112 | 0.9802   |
| 0.0342        | 3.0   | 11250 | 0.0593          | 0.9174    | 0.9172 | 0.9173 | 0.9815   |
| 0.0253        | 4.0   | 15000 | 0.0634          | 0.9204    | 0.9200 | 0.9202 | 0.9818   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
