Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
text-embeddings-inference
Instructions to use Joshwabail/distilbert-absa-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joshwabail/distilbert-absa-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Joshwabail/distilbert-absa-sequence-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Joshwabail/distilbert-absa-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("Joshwabail/distilbert-absa-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Joshwabail/distilbert-absa-sequence-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/Joshwabail/distilbert-absa-sequence-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Joshwabail/distilbert-absa-sequence-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Joshwabail/distilbert-absa-sequence-classification/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 3d4bc8b051803147067c0f70e2fb00f53add8766bd99c238a8b321b2b6bd7edd
- Size of remote file:
- 3.52 kB
- SHA256:
- 9d17d9ffd5f4d119fb50990ab0148024c8aded37b5f6f0e8ce19f74417cc9dac
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