Instructions to use modularStarEncoder/ModularStarEncoder-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder-finetuned", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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You will get as an output three elements:
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- projected_pooled_normalized: a list of the projected, pooled, and normalized embeddings from the five exit points;
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- raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
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- attentions: attention scores from the encoder
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You will get as an output three elements:
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- projected_pooled_normalized: a list of the projected, pooled, and normalized embeddings from the five exit points (respectively from layers [4,9,18,27,36], the last element of the list corresponds to the final layer projected representation);
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- raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
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- attentions: attention scores from the encoder
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