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
      config: emotion
      split: train
      args: emotion
    metrics:
    - type: precision
      value: 0.7350080900694398
      name: Precision
    - type: recall
      value: 0.7334480130231172
      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: 1.1951
- Precision: 0.7350
- Recall: 0.7334
- Fscore: 0.7341

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8468        | 1.0   | 815  | 0.7465          | 0.7116    | 0.6096 | 0.6325 |
| 0.5105        | 2.0   | 1630 | 0.9035          | 0.7532    | 0.7111 | 0.7276 |
| 0.2492        | 3.0   | 2445 | 1.1951          | 0.7350    | 0.7334 | 0.7341 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
