Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/distilbert-base-uncased_fold_2_binary_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/distilbert-base-uncased_fold_2_binary_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/distilbert-base-uncased_fold_2_binary_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/distilbert-base-uncased_fold_2_binary_v1") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/distilbert-base-uncased_fold_2_binary_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from elopezlopez/distilbert-base-uncased_fold_2_binary_v1: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_2_binary_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://elopezlopez/distilbert-base-uncased_fold_2_binary_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/elopezlopez/distilbert-base-uncased_fold_2_binary_v1/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- e4d07e44a47590adfcd0470b7fb7e51492703dc38056d1b84b785141fe68e63c
- Size of remote file:
- 3.38 kB
- SHA256:
- 71e57941b4a4a72dc44e5ab8ea2f93fbe617dc91db0db4b60a508e6bee60515c
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