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--- |
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tags: |
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- target-identification |
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- argumentation |
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- contrastive-learning |
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license: mit |
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language: |
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- en |
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base_model: |
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- answerdotai/ModernBERT-base |
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pipeline_tag: text-classification |
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--- |
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: |
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--- |
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## Model Description |
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This is a dual-encoder retrieval model built on top of `answerdotai/ModernBERT-base`. The model is designed to perform target identification by finding the most relevant `theses` along with their associated data for a given `claim` |
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You can modify the `top_k`, `num_args` & `top_level_only` variables to adjust the output of the model. |
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## How to use |
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You can use this model for inference by loading it with the `transformers` library. The following code demonstrates how to make a prediction: |
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```python |
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import torch |
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import torch.nn as nn |
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from transformers import AutoModel, AutoTokenizer |
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from huggingface_hub import hf_hub_download, PyTorchModelHubMixin |
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import pickle |
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from sklearn.metrics.pairwise import cosine_similarity |
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import numpy as np |
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class DualEncoderThesisModel(nn.Module, PyTorchModelHubMixin): |
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def __init__(self) -> None: |
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super(DualEncoderThesisModel, self).__init__() |
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self.encoder = AutoModel.from_pretrained("answerdotai/ModernBERT-base") |
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def forward(self, input_ids_a, attention_mask_a, input_ids_b, attention_mask_b): |
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# Encode arguments |
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output_a = self.encoder(input_ids=input_ids_a, attention_mask=attention_mask_a).last_hidden_state |
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emb_a = output_a[:, 0] |
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# Encode theses |
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output_b = self.encoder(input_ids=input_ids_b, attention_mask=attention_mask_b).last_hidden_state |
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emb_b = output_b[:, 0] |
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return emb_a, emb_b |
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model_name = "azza1625/target-identification" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = DualEncoderThesisModel.from_pretrained(model_name) |
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model.eval() |
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device = "cpu" |
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embeddings_path = hf_hub_download( |
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repo_id="azza1625/target-identification", |
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filename="retrieval_data_random_negatives_10_train_data.pkl" |
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) |
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with open(embeddings_path, "rb") as f: |
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embeddings_metadata = pickle.load(f) |
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@torch.no_grad() |
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def retrieve_theses(claim, top_k=3, num_args=5, top_level_only=True, device="cpu"): |
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stored_embeddings = embeddings_metadata["embeddings"] |
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metadata = embeddings_metadata["metadata"] |
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enc = tokenizer(claim, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device) |
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query_embedding = model.encoder(**enc).last_hidden_state[:, 0].cpu().numpy() |
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sims = cosine_similarity(query_embedding, stored_embeddings)[0] |
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top_indices = np.argsort(sims)[::-1][:top_k] |
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results = [] |
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for idx in top_indices: |
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arguments = metadata[idx]['arguments'] |
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if top_level_only: |
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arguments = [arg for arg in arguments if arg['target_type'] == 'thesis'] |
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results.append({ |
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"thesis": metadata[idx]["thesis"], |
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"debate_title": metadata[idx]["debate_title"], |
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"arguments": arguments[:num_args] |
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}) |
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return results |
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claim = "A fetus or embryo is not a person; therefore, abortion should not be considered murder." |
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theses = retrieve_theses(claim) |
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for thesis in theses: |
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print(f"{thesis['thesis']} | {thesis['debate_title']}") |
