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---
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language:
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- en
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license: apache-2.0
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datasets:
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- wikimedia/wikipedia
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- bookcorpus
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- nomic-ai/nomic-bert-2048-pretraining-data
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inference: false
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---
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# nomic-bert-2048: A 2048 Sequence Length Pretrained BERT
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`nomic-bert-2048` is a BERT model pretrained on `wikipedia` and `bookcorpus` with a max sequence length of 2048.
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We make several modifications to our BERT training procedure similar to [MosaicBERT](https://www.databricks.com/blog/mosaicbert).
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Namely, we add:
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- Use [Rotary Position Embeddings](https://arxiv.org/pdf/2104.09864.pdf) to allow for context length extrapolation.
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- Use SwiGLU activations as it has [been shown](https://arxiv.org/abs/2002.05202) to [improve model performance](https://www.databricks.com/blog/mosaicbert)
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- Set dropout to 0
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We evaluate the quality of nomic-bert-2048 on the standard [GLUE](https://gluebenchmark.com/) benchmark. We find
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it performs comparably to other BERT models but with the advantage of a significantly longer context length.
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| Model | Bsz | Steps | Seq | Avg | Cola | SST2 | MRPC | STSB | QQP | MNLI | QNLI | RTE |
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|-------------|-----|-------|-------|----------|----------|----------|------|------|------|------|------|------|
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| NomicBERT | 4k | 100k | 2048 | 0.84 | 0.50 | 0.93 | 0.88 | 0.90 | 0.92 | 0.86 | 0.92 | 0.82 |
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| RobertaBase | 8k | 500k | 512 | 0.86 | 0.64 | 0.95 | 0.90 | 0.91 | 0.92 | 0.88 | 0.93 | 0.79 |
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| JinaBERTBase| 4k | 100k | 512 | 0.83 | 0.51 | 0.95 | 0.88 | 0.90 | 0.81 | 0.86 | 0.92 | 0.79 |
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| MosaicBERT | 4k | 178k | 128 | 0.85 | 0.59 | 0.94 | 0.89 | 0.90 | 0.92 | 0.86 | 0.91 | 0.83 |
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## Pretraining Data
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We use [BookCorpus](https://huggingface.co/datasets/bookcorpus) and a 2023 dump of [wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia).
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We pack and tokenize the sequences to 2048 tokens. If a document is shorter than 2048 tokens, we append another document until it fits 2048 tokens.
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If a document is greater than 2048 tokens, we split it across multiple documents. We release the dataset [here](https://huggingface.co/datasets/nomic-ai/nomic-bert-2048-pretraining-data/)
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# Usage
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```python
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from transformers import AutoModelForMaskedLM, AutoConfig, AutoTokenizer, pipeline
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # `nomic-bert-2048` uses the standard BERT tokenizer
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config = AutoConfig.from_pretrained('nomic-ai/nomic-bert-2048', trust_remote_code=True) # the config needs to be passed in
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model = AutoModelForMaskedLM.from_pretrained('nomic-ai/nomic-bert-2048',config=config, trust_remote_code=True)
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# To use this model directly for masked language modeling
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classifier = pipeline('fill-mask', model=model, tokenizer=tokenizer,device="cpu")
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print(classifier("I [MASK] to the store yesterday."))
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```
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To finetune the model for a Sequence Classification task, you can use the following snippet
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```python
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from transformers import AutoConfig, AutoModelForSequenceClassification
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model_path = "nomic-ai/nomic-bert-2048"
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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# strict needs to be false here since we're initializing some new params
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model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config, trust_remote_code=True, strict=False)
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```
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# Join the Nomic Community
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- Nomic: [https://nomic.ai](https://nomic.ai)
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- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
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- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) |
