Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/prova_Classi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/prova_Classi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/prova_Classi2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/prova_Classi2") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/prova_Classi2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from marcolatella/prova_Classi2: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/marcolatella/prova_Classi2/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://marcolatella/prova_Classi2@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/marcolatella/prova_Classi2/resolve/refs%2Fpr%2F1/README.md
1.59 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - tweet_eval | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: prova_Classi2 | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: tweet_eval | |
| type: tweet_eval | |
| args: sentiment | |
| metrics: | |
| - name: F1 | |
| type: f1 | |
| value: 0.20192866271639365 | |
| <!-- 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. --> | |
| # prova_Classi2 | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0183 | |
| - F1: 0.2019 | |
| ## 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: 0.002739353542073378 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 16 | |
| - seed: 18 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 1.0171 | 1.0 | 1426 | 1.0183 | 0.2019 | | |
| ### Framework versions | |
| - Transformers 4.13.0 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.16.1 | |
| - Tokenizers 0.10.3 | |