|
--- |
|
license: mit |
|
base_model: microsoft/mdeberta-v3-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- tweet_sentiment_multilingual |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all |
|
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: 0.6512345679012346 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6483011417314103 |
|
--- |
|
|
|
<!-- 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-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all |
|
|
|
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tweet_sentiment_multilingual dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.7268 |
|
- Accuracy: 0.6512 |
|
- F1: 0.6483 |
|
|
|
## 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: 64 |
|
- seed: 66 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 0.8937 | 1.09 | 500 | 0.8922 | 0.6304 | 0.6189 | |
|
| 0.6912 | 2.17 | 1000 | 0.8900 | 0.6551 | 0.6516 | |
|
| 0.527 | 3.26 | 1500 | 0.9088 | 0.6593 | 0.6583 | |
|
| 0.3874 | 4.35 | 2000 | 1.1089 | 0.6516 | 0.6470 | |
|
| 0.2977 | 5.43 | 2500 | 1.2137 | 0.6408 | 0.6433 | |
|
| 0.2397 | 6.52 | 3000 | 1.2022 | 0.6431 | 0.6409 | |
|
| 0.203 | 7.61 | 3500 | 1.4913 | 0.6454 | 0.6469 | |
|
| 0.1658 | 8.7 | 4000 | 1.7268 | 0.6512 | 0.6483 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
