Instructions to use AmedeoBonatti/nlp_te_cls_matbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmedeoBonatti/nlp_te_cls_matbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AmedeoBonatti/nlp_te_cls_matbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AmedeoBonatti/nlp_te_cls_matbert") model = AutoModelForSequenceClassification.from_pretrained("AmedeoBonatti/nlp_te_cls_matbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nlp_te_cls_matbert
This model is a fine-tuned version of AmedeoBonatti/nlp_te_mlm_matbert on the None dataset.
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 21
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.49.0
- Pytorch 2.2.1
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for AmedeoBonatti/nlp_te_cls_matbert
Base model
AmedeoBonatti/nlp_te_mlm_matbert