---
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: sentiment-roberta-clean-e8-b16-data2
  results: []
---

<!-- 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. -->

# sentiment-roberta-clean-e8-b16-data2

This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9974
- F1: 0.7709
- Recall: 0.7709
- Precision: 0.7709

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| No log        | 1.0   | 375  | 0.7813          | 0.7412 | 0.7412 | 0.7412    |
| 0.5973        | 2.0   | 750  | 0.7826          | 0.7332 | 0.7332 | 0.7332    |
| 0.2098        | 3.0   | 1125 | 1.1970          | 0.7547 | 0.7547 | 0.7547    |
| 0.1231        | 4.0   | 1500 | 1.5843          | 0.7520 | 0.7520 | 0.7520    |
| 0.1231        | 5.0   | 1875 | 1.7089          | 0.7520 | 0.7520 | 0.7520    |
| 0.0457        | 6.0   | 2250 | 1.7845          | 0.7601 | 0.7601 | 0.7601    |
| 0.0228        | 7.0   | 2625 | 2.0039          | 0.7601 | 0.7601 | 0.7601    |
| 0.0134        | 8.0   | 3000 | 1.9974          | 0.7709 | 0.7709 | 0.7709    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3