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---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV_data-cardiffnlp_tweet_sentiment_multilingual_all_delta
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_sentiment_multilingual
type: tweet_sentiment_multilingual
config: all
split: validation
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.3333333333333333
- name: F1
type: f1
value: 0.16666666666666666
---
<!-- 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. -->
# scenario-TCR-XLMV_data-cardiffnlp_tweet_sentiment_multilingual_all_delta
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0987
- Accuracy: 0.3333
- F1: 0.1667
## 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: 32
- eval_batch_size: 32
- seed: 11213
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0872 | 1.09 | 500 | 1.0884 | 0.3978 | 0.3194 |
| 1.0993 | 2.17 | 1000 | 1.0988 | 0.3333 | 0.1667 |
| 1.0983 | 3.26 | 1500 | 1.1525 | 0.3329 | 0.1713 |
| 1.0994 | 4.35 | 2000 | 1.0997 | 0.3333 | 0.1667 |
| 1.0998 | 5.43 | 2500 | 1.0991 | 0.3333 | 0.1667 |
| 1.0997 | 6.52 | 3000 | 1.0987 | 0.3333 | 0.1667 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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