---
library_name: transformers
license: mit
base_model: cardiffnlp/twitter-roberta-large-hate-latest
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: twitter-roberta-large-hate-latest-hinglish-binary
  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. -->

# twitter-roberta-large-hate-latest-hinglish-binary

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-large-hate-latest) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6112
- Accuracy: 0.6878
- Precision: 0.6586
- Recall: 0.6291
- F1: 0.6330

## 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6312        | 0.9709 | 25   | 0.5962          | 0.6921   | 0.7303    | 0.5882 | 0.5657 |
| 0.5494        | 1.9806 | 51   | 0.6137          | 0.7084   | 0.7402    | 0.6140 | 0.6047 |
| 0.5448        | 2.9903 | 77   | 0.5723          | 0.7221   | 0.7021    | 0.6701 | 0.6771 |
| 0.5016        | 4.0    | 103  | 0.5903          | 0.7030   | 0.6783    | 0.6470 | 0.6524 |
| 0.4918        | 4.8544 | 125  | 0.5932          | 0.7084   | 0.6832    | 0.6610 | 0.6666 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0