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---
license: mit
base_model: neuraly/bert-base-italian-cased-sentiment
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
model-index:
- name: sentiment_ita
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_sentiment_multilingual
type: tweet_sentiment_multilingual
config: italian
split: validation
args: italian
metrics:
- name: Accuracy
type: accuracy
value: 0.6851851851851852
---
<!-- 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. -->
# sentiment_ita
This model is a fine-tuned version of [neuraly/bert-base-italian-cased-sentiment](https://huggingface.co/neuraly/bert-base-italian-cased-sentiment) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5199
- Accuracy: 0.6852
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 170 | 1.1582 | 0.6173 |
| No log | 2.0 | 340 | 0.8326 | 0.6389 |
| 1.0735 | 3.0 | 510 | 0.7827 | 0.6543 |
| 1.0735 | 4.0 | 680 | 0.7898 | 0.6728 |
| 1.0735 | 5.0 | 850 | 0.8674 | 0.6759 |
| 0.4509 | 6.0 | 1020 | 1.0103 | 0.6883 |
| 0.4509 | 7.0 | 1190 | 1.1162 | 0.7006 |
| 0.4509 | 8.0 | 1360 | 1.3433 | 0.6883 |
| 0.1439 | 9.0 | 1530 | 1.4674 | 0.6821 |
| 0.1439 | 10.0 | 1700 | 1.5199 | 0.6852 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|