Instructions to use davideaguglia/provaESM-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davideaguglia/provaESM-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="davideaguglia/provaESM-finetuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("davideaguglia/provaESM-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("davideaguglia/provaESM-finetuned-imdb") - Notebooks
- Google Colab
- Kaggle
provaESM-finetuned-imdb
This model is a fine-tuned version of facebook/esm2_t6_8M_UR50D 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for davideaguglia/provaESM-finetuned-imdb
Base model
facebook/esm2_t6_8M_UR50D