Text Classification
Transformers
PyTorch
English
distilbert
text-classfication
nlp
neural-compressor
PostTrainingDynamic
int8
Intel® Neural Compressor
text-embeddings-inference
Instructions to use Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc") model = AutoModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-MRPC-int8-dynamic-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 438f7a9ac075377e50f040d4ee17ba56e2f95a94053fc860de69dc5d6b988520
- Size of remote file:
- 75.2 MB
- SHA256:
- 77bf55a72c5cf09e786d954a598e683de34b1a2251646a4fdea7271d754f8e07
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