Text Classification
Transformers
PyTorch
rita
protein language model
Generated from Trainer
custom_code
Eval Results (legacy)
Instructions to use thundaa/tape-fluorescence-prediction-RITA_s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thundaa/tape-fluorescence-prediction-RITA_s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thundaa/tape-fluorescence-prediction-RITA_s", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("thundaa/tape-fluorescence-prediction-RITA_s", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- train
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metrics:
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model-index:
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- name: tape-fluorescence-prediction-RITA_s
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: cradle-bio/tape-fluorescence
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type: train
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metrics:
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type: spearmanr
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value: 0.2955275250425323
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- train
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metrics:
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- spearmanr
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base_model: lightonai/RITA_s
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model-index:
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- name: tape-fluorescence-prediction-RITA_s
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: cradle-bio/tape-fluorescence
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type: train
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metrics:
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- type: spearmanr
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value: 0.2955275250425323
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name: Spearmanr
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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