Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use deathperminutV2/NLP_sequence_clasiffication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deathperminutV2/NLP_sequence_clasiffication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deathperminutV2/NLP_sequence_clasiffication")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deathperminutV2/NLP_sequence_clasiffication") model = AutoModelForSequenceClassification.from_pretrained("deathperminutV2/NLP_sequence_clasiffication") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:28a61d3962a58b8e64151b5c3698760560055a75365b7b7534e8b397152cf7cf
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size 328496480
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