transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:twitter-sentiment-analysis license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- twitter-sentiment-analysis
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-sent-analysis
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: twitter-sentiment-analysis
      type: twitter-sentiment-analysis
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.8475847584758476
      name: Accuracy
---

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# bert-finetuned-sent-analysis

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6462
- Accuracy: 0.8476

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4013        | 1.0   | 14999 | 0.3686          | 0.8394   |
| 0.3354        | 2.0   | 29998 | 0.4543          | 0.8493   |
| 0.2539        | 3.0   | 44997 | 0.6462          | 0.8476   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
