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--- |
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license: mit |
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--- |
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this is the placeholder model card for the (finess-benchmark-space)[https://huggingface.co/spaces/enzoescipy/finesse-benchmark-space] and its (database)[https://huggingface.co/datasets/enzoescipy/finesse-benchmark-results]. |
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```python |
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import torch |
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from typing import List |
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from transformers import AutoConfig, PreTrainedModel # Optional: for loading configs |
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from finesse_benchmark.interfaces import FinesseSynthesizer |
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# --- Custom Embedder Example --- |
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# Uncomment and customize this class for your embedder. |
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class AverageSynthesizer(FinesseSynthesizer): |
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""" |
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Average Synthesizer: Computes the mean of input embeddings without using any model. |
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""" |
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def __init__(self, config_path: str): |
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super().__init__() |
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# No model to load for average pooling |
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print(f"{self.__class__.__name__} initialized - Average pooling ready.") |
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def synthesize(self, embeddings: torch.Tensor, **kwargs) -> torch.Tensor: |
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""" |
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Average synthesis: Compute the mean along the sequence dimension. |
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Args: |
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embeddings: torch.Tensor of shape (batch, seq_len, embedding_dim) |
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**kwargs: Additional arguments |
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Returns: |
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torch.Tensor of shape (batch, embedding_dim) |
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""" |
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return embeddings.mean(dim=1) |
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def device(self): |
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return "cpu" |
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``` |
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it just averages the embedding vectors. |
