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Priyanka-Balivada
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bert-5-epoch-sentiment

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Model card Files Community
1
bert-5-epoch-sentiment
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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- precision
- recall
model-index:
- name: bert-5-epoch-sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: sentiment
      split: test
      args: sentiment
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6754314555519375
    - name: Precision
      type: precision
      value: 0.6779994190554874
    - name: Recall
      type: recall
      value: 0.6754314555519375
---

<!-- 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. -->

# bert-5-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: 2.5187
- Accuracy: 0.6754
- Precision: 0.6780
- Recall: 0.6754
- Micro-avg-recall: 0.6754
- Micro-avg-precision: 0.6754

## 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:----------------:|:-------------------:|
| 0.0381        | 1.0   | 2851  | 2.4402          | 0.6588   | 0.6676    | 0.6588 | 0.6588           | 0.6588              |
| 0.0401        | 2.0   | 5702  | 2.7499          | 0.6527   | 0.6647    | 0.6527 | 0.6527           | 0.6527              |
| 0.1609        | 3.0   | 8553  | 2.0380          | 0.6687   | 0.6724    | 0.6687 | 0.6687           | 0.6687              |
| 0.1811        | 4.0   | 11404 | 2.3206          | 0.6679   | 0.6753    | 0.6679 | 0.6679           | 0.6679              |
| 0.0987        | 5.0   | 14255 | 2.5187          | 0.6754   | 0.6780    | 0.6754 | 0.6754           | 0.6754              |


### Framework versions

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