Instructions to use Taykhoom/SpliceBERT-1024nt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/SpliceBERT-1024nt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/SpliceBERT-1024nt", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/SpliceBERT-1024nt", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -119,6 +119,11 @@ This port uses [BERT-updated](https://huggingface.co/Taykhoom/BERT-updated), whi
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adds `attn_implementation="sdpa"` and `attn_implementation="flash_attention_2"` support
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```python
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model = AutoModel.from_pretrained("Taykhoom/SpliceBERT-1024nt",
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trust_remote_code=True,
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adds `attn_implementation="sdpa"` and `attn_implementation="flash_attention_2"` support
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not present in the original codebase.
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The pooler weights (`pooler.dense`) are not present in the original checkpoint and are
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not included in the saved `model.safetensors`. `add_pooling_layer=True` (the default)
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allocates the pooler layer but its weights are randomly initialized -- do not use
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`pooler_output` without fine-tuning.
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```python
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model = AutoModel.from_pretrained("Taykhoom/SpliceBERT-1024nt",
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trust_remote_code=True,
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