Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use luigisaetta/haspeech_ita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luigisaetta/haspeech_ita with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="luigisaetta/haspeech_ita")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("luigisaetta/haspeech_ita") model = AutoModelForSequenceClassification.from_pretrained("luigisaetta/haspeech_ita") - Notebooks
- Google Colab
- Kaggle
haspeech_ita
This model is a fine-tuned version of MilaNLProc/hate-ita-xlm-r-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0250
- F1 Macro: 0.9966
- Accuracy: 0.9973
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: 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: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
|---|---|---|---|---|---|
| 0.4001 | 0.48 | 100 | 0.1722 | 0.9198 | 0.9377 |
| 0.1669 | 0.96 | 200 | 0.0355 | 0.9865 | 0.9892 |
| 0.0824 | 1.44 | 300 | 0.0839 | 0.9864 | 0.9892 |
| 0.0672 | 1.92 | 400 | 0.0277 | 0.9933 | 0.9946 |
| 0.0761 | 2.4 | 500 | 0.0483 | 0.9933 | 0.9946 |
| 0.0413 | 2.88 | 600 | 0.0250 | 0.9966 | 0.9973 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for luigisaetta/haspeech_ita
Base model
MilaNLProc/hate-ita-xlm-r-large