Instructions to use hf-tiny-model-private/tiny-random-EsmForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-EsmForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-EsmForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-EsmForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "clean_up_tokenization_spaces": true, | |
| "model_max_length": 512, | |
| "special_tokens_map_file": "/home/runner/.cache/huggingface/hub/models--facebook--esm-1b/snapshots/9d4d0ba06814338846762f17500f13bd17c1f8ce/special_tokens_map.json", | |
| "tokenizer_class": "EsmTokenizer" | |
| } | |