---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twitter_xlm_robertta_sentiment_financial_news
  results: []
datasets:
- Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# twitter_xlm_robertta_sentiment_financial_news

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on [this]()https://huggingface.co/datasets/Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75 financial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4492
- F1: 0.8812

## 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: 5e-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
- lr_scheduler_warmup_steps: 300
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.518         | 1.0   | 556  | 0.4881          | 0.8184 |
| 0.3534        | 2.0   | 1112 | 0.5041          | 0.8797 |
| 0.1781        | 3.0   | 1668 | 0.4492          | 0.8812 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.1