Instructions to use moshew/bert-mini-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/bert-mini-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/bert-mini-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/bert-mini-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/bert-mini-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from moshew/bert-mini-sst2-distilled: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/moshew/bert-mini-sst2-distilled/resolve/main/training_args.bin
- Command line
-
hf download hf://moshew/bert-mini-sst2-distilled/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/moshew/bert-mini-sst2-distilled/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 955d4ff9b3a0c1d02b1eb338e65061beae15d9a164a0cdad91efacaa4041d3d2
- Size of remote file:
- 3.12 kB
- SHA256:
- 3f2669e2d0bccb05f018bd1ccfe717a133adff1bda9b1935adff03844da709bf
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