---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: fineTuningXLMRoberta-TokenClassification-Spacy
  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. -->

# fineTuningXLMRoberta-TokenClassification-Spacy

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8479
- Precision: 0.2076
- Recall: 0.2102
- F1: 0.2089
- Accuracy: 0.6718

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 31   | 0.7433          | 0.2164    | 0.1421 | 0.1716 | 0.6557   |
| No log        | 2.0   | 62   | 0.7177          | 0.2275    | 0.1848 | 0.2039 | 0.6727   |
| No log        | 3.0   | 93   | 0.7054          | 0.1719    | 0.1949 | 0.1827 | 0.6637   |
| No log        | 4.0   | 124  | 0.7148          | 0.1823    | 0.1919 | 0.1869 | 0.6628   |
| No log        | 5.0   | 155  | 0.7018          | 0.2063    | 0.2061 | 0.2062 | 0.6853   |
| No log        | 6.0   | 186  | 0.7310          | 0.1866    | 0.1919 | 0.1892 | 0.6711   |
| No log        | 7.0   | 217  | 0.7272          | 0.2150    | 0.2071 | 0.2110 | 0.6897   |
| No log        | 8.0   | 248  | 0.7878          | 0.1758    | 0.1848 | 0.1802 | 0.6582   |
| No log        | 9.0   | 279  | 0.7727          | 0.2080    | 0.2071 | 0.2075 | 0.6814   |
| No log        | 10.0  | 310  | 0.8099          | 0.1969    | 0.1959 | 0.1964 | 0.6688   |
| No log        | 11.0  | 341  | 0.8119          | 0.2062    | 0.2030 | 0.2046 | 0.6766   |
| No log        | 12.0  | 372  | 0.8227          | 0.2105    | 0.2112 | 0.2108 | 0.6770   |
| No log        | 13.0  | 403  | 0.8300          | 0.2008    | 0.2051 | 0.2029 | 0.6744   |
| No log        | 14.0  | 434  | 0.8409          | 0.2064    | 0.2081 | 0.2073 | 0.6739   |
| No log        | 15.0  | 465  | 0.8479          | 0.2076    | 0.2102 | 0.2089 | 0.6718   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1