transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:tweet_eval license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      args: emotion
    metrics:
    - type: precision
      value: 0.7295622072449084
      name: Precision
    - type: recall
      value: 0.7265951560457381
      name: Recall
---

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# bert-emotion

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1531
- Precision: 0.7296
- Recall: 0.7266
- Fscore: 0.7278

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8418        | 1.0   | 815  | 0.8129          | 0.7960    | 0.6242 | 0.6420 |
| 0.5222        | 2.0   | 1630 | 0.9663          | 0.7584    | 0.7196 | 0.7324 |
| 0.2662        | 3.0   | 2445 | 1.1531          | 0.7296    | 0.7266 | 0.7278 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
