transformers pytorch bert text-classification generated_from_trainer en dataset:tweet_eval base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- tweet_eval
language:
- en
license: apache-2.0
metrics:
- accuracy
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-3-epoch-sentiment
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: sentiment
      split: test
      args: sentiment
    metrics:
    - type: accuracy
      value: 0.6746173884728102
      name: Accuracy
    - type: precision
      value: 0.6775838191417041
      name: Precision
    - type: recall
      value: 0.6746173884728102
      name: Recall
---

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# bert-3-epoch-sentiment

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5772
- Accuracy: 0.6746
- Precision: 0.6776
- Recall: 0.6746
- Micro-avg-recall: 0.6746
- Micro-avg-precision: 0.6746

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:----------------:|:-------------------:|
| 0.1114        | 1.0   | 2851 | 2.2420          | 0.6662   | 0.6705    | 0.6662 | 0.6662           | 0.6662              |
| 0.0949        | 2.0   | 5702 | 2.5923          | 0.6539   | 0.6676    | 0.6539 | 0.6539           | 0.6539              |
| 0.2172        | 3.0   | 8553 | 2.5772          | 0.6746   | 0.6776    | 0.6746 | 0.6746           | 0.6746              |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

### Author

[Priyanka Balivada]()