File size: 1,875 Bytes
084dbc5 868401e 5aaa717 084dbc5 868401e 5aaa717 868401e 5aaa717 868401e d151a08 5aaa717 8ed98a2 868401e 5aaa717 771d973 868401e 5aaa717 868401e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
license: mit
base_model: neuraly/bert-base-italian-cased-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment_ita
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. -->
# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6301
- Accuracy: 0.6903
## 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: 48
- 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: 600
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5002 | 1.67 | 100 | 0.9894 | 0.6460 |
| 0.6959 | 3.33 | 200 | 0.7681 | 0.6726 |
| 0.5325 | 5.0 | 300 | 0.7615 | 0.6962 |
| 0.349 | 6.67 | 400 | 0.8867 | 0.6932 |
| 0.1798 | 8.33 | 500 | 1.1361 | 0.6873 |
| 0.0983 | 10.0 | 600 | 1.3994 | 0.6962 |
| 0.0412 | 11.67 | 700 | 1.5411 | 0.7109 |
| 0.0293 | 13.33 | 800 | 1.6301 | 0.6903 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|