Text Classification
Transformers
Safetensors
lfm2
feature-extraction
betterwright
accessibility
browser-agent
reranking
long-context
custom_code
Instructions to use ProCreations/betterwright-encoder-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/betterwright-encoder-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProCreations/betterwright-encoder-350m", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ProCreations/betterwright-encoder-350m", trust_remote_code=True) model = AutoModel.from_pretrained("ProCreations/betterwright-encoder-350m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix remote-code sibling import
Browse files- modeling_betterwright.py +1 -1
modeling_betterwright.py
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@@ -12,7 +12,7 @@ from transformers.modeling_outputs import ModelOutput
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from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
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from transformers.models.lfm2.modeling_lfm2 import Lfm2PreTrainedModel
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from modeling_lfm2_bidirectional import Lfm2BidirectionalModel, _install_patches
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@dataclass
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from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
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from transformers.models.lfm2.modeling_lfm2 import Lfm2PreTrainedModel
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from .modeling_lfm2_bidirectional import Lfm2BidirectionalModel, _install_patches
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@dataclass
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