---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-meta4types-ft
  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. -->

# xlm-roberta-meta4types-ft

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co./cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8324
- Roc Auc: 0.7122
- Hamming Loss: 0.2261
- F1 Score: 0.6089
- Accuracy: 0.5528
- Precision: 0.6081
- Recall: 0.6436
- Per Label: {'f1_score': 0.608905822183525, 'precision': 0.6080571799870046, 'recall': 0.6435841440010588, 'support': 235}

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Roc Auc | Hamming Loss | F1 Score | Accuracy | Precision | Recall | Per Label                                                                                                         |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:--------:|:--------:|:---------:|:------:|:-----------------------------------------------------------------------------------------------------------------:|
| 0.4279        | 1.0   | 199  | 0.5287          | 0.4967  | 0.2496       | 0.3209   | 0.5276   | 0.6759    | 0.3575 | {'f1_score': 0.3208852937872149, 'precision': 0.6759286629224553, 'recall': 0.35748792270531404, 'support': 235}  |
| 0.4609        | 2.0   | 398  | 0.5076          | 0.5276  | 0.2245       | 0.3757   | 0.5779   | 0.8026    | 0.3913 | {'f1_score': 0.3757246741060956, 'precision': 0.8025944726452341, 'recall': 0.3913043478260869, 'support': 235}   |
| 0.5875        | 3.0   | 597  | 0.5463          | 0.5557  | 0.2127       | 0.4232   | 0.6080   | 0.6653    | 0.4153 | {'f1_score': 0.42320834457332973, 'precision': 0.6653348029760265, 'recall': 0.41534974521871487, 'support': 235} |
| 0.493         | 4.0   | 796  | 0.5526          | 0.6428  | 0.2077       | 0.5744   | 0.6080   | 0.6577    | 0.5455 | {'f1_score': 0.5744086944086945, 'precision': 0.6577216876443267, 'recall': 0.5455495996294091, 'support': 235}   |
| 0.3519        | 5.0   | 995  | 0.6760          | 0.6795  | 0.2161       | 0.5809   | 0.5879   | 0.6192    | 0.5961 | {'f1_score': 0.5809003977320809, 'precision': 0.6191632544737641, 'recall': 0.5960790152868771, 'support': 235}   |
| 0.2451        | 6.0   | 1194 | 0.7729          | 0.7046  | 0.2312       | 0.6045   | 0.5578   | 0.6161    | 0.6045 | {'f1_score': 0.6045152483631816, 'precision': 0.6161038489469862, 'recall': 0.6044603269141685, 'support': 235}   |
| 0.0608        | 7.0   | 1393 | 0.7616          | 0.6942  | 0.2127       | 0.6060   | 0.5779   | 0.6221    | 0.6095 | {'f1_score': 0.6060266030810951, 'precision': 0.6220689655172414, 'recall': 0.6094566871815233, 'support': 235}   |
| 0.0859        | 8.0   | 1592 | 0.8324          | 0.7122  | 0.2261       | 0.6089   | 0.5528   | 0.6081    | 0.6436 | {'f1_score': 0.608905822183525, 'precision': 0.6080571799870046, 'recall': 0.6435841440010588, 'support': 235}    |
| 0.0767        | 9.0   | 1791 | 0.8192          | 0.6950  | 0.2127       | 0.6004   | 0.5578   | 0.6086    | 0.6073 | {'f1_score': 0.6003549503292779, 'precision': 0.6086247086247086, 'recall': 0.6072827741380452, 'support': 235}   |
| 0.0221        | 10.0  | 1990 | 0.8094          | 0.6975  | 0.2077       | 0.6135   | 0.5578   | 0.6116    | 0.6215 | {'f1_score': 0.6135398054397458, 'precision': 0.6116043923140263, 'recall': 0.6215108199324995, 'support': 235}   |


### Framework versions

- Transformers 4.43.1
- Pytorch 1.13.1+cu116
- Datasets 2.20.0
- Tokenizers 0.19.1