|
--- |
|
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 |
|
|
