M2-BERT Embeddings
Collection
Models and Datasets for M2-BERT and LoCoV1
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10 items
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Updated
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2
The 80M checkpoint for M2-BERT-8k from the paper Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT.
Check out our GitHub for instructions on how to download and fine-tune it!
You can load this model using Hugging Face AutoModel
:
from transformers import AutoModelForMaskedLM, BertConfig
config = BertConfig.from_pretrained("hazyresearch/M2-BERT-8K-Retrieval-Encoder-V1")
model = AutoModelForMaskedLM.from_pretrained("hazyresearch/M2-BERT-8K-Retrieval-Encoder-V1", config=config,trust_remote_code=True)
This model uses the Hugging Face bert-base-uncased tokenizer
:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
This model generates embeddings for retrieval. The embeddings have a dimensionality of 768:
from transformers import AutoTokenizer, AutoModelForMaskedLM, BertConfig
max_seq_length = 8192
testing_string = "Every morning, I make a cup of coffee to start my day."
config = BertConfig.from_pretrained("hazyresearch/M2-BERT-8K-Retrieval-Encoder-V1")
model = AutoModelForMaskedLM.from_pretrained("hazyresearch/M2-BERT-8K-Retrieval-Encoder-V1", config=config, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length)
input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length)
outputs = model(**input_ids)
embeddings = outputs['sentence_embedding']
This model requires trust_remote_code=True
to be passed to the from_pretrained
method. This is because we use custom PyTorch code (see our GitHub). You should consider passing a revision
argument that specifies the exact git commit of the code, for example:
mlm = AutoModelForMaskedLM.from_pretrained(
"hazyresearch/M2-BERT-8K-Retrieval-Encoder-V1",
config=config,
trust_remote_code=True,
)
Note use_flash_mm
is false by default. Using FlashMM is currently not supported.