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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: sentence-similarity |
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inference: false |
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--- |
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# Monarch Mixer-BERT |
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The 80M checkpoint for M2-BERT-base from the paper [Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture](https://arxiv.org/abs/2310.12109). |
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This model has been pretrained with sequence length 2048, and it has been fine-tuned for long-context retrieval. |
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This model was trained by Jon Saad-Falcon, Dan Fu, and Simran Arora. |
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Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it! |
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## How to use |
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You can load this model using Hugging Face `AutoModel`: |
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```python |
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from transformers import AutoModelForMaskedLM |
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model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-2k-retrieval", trust_remote_code=True) |
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``` |
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This model generates embeddings for retrieval. The embeddings have a dimensionality of 768: |
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``` |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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max_seq_length = 2048 |
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testing_string = "Every morning, I make a cup of coffee to start my day." |
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model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-2k-retrieval", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length) |
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input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length) |
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outputs = model(**input_ids) |
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embeddings = outputs['sentence_embedding'] |
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``` |
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