Instructions to use iarfmoose/bert-base-cased-qa-evaluator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iarfmoose/bert-base-cased-qa-evaluator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iarfmoose/bert-base-cased-qa-evaluator")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iarfmoose/bert-base-cased-qa-evaluator") model = AutoModelForSequenceClassification.from_pretrained("iarfmoose/bert-base-cased-qa-evaluator", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from iarfmoose/bert-base-cased-qa-evaluator: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://iarfmoose/bert-base-cased-qa-evaluator/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- aadab725f07dc0bc65bf662a6576e04c18e64a24bd027f4a29899e3568ea4f5e
- Size of remote file:
- 433 MB
- SHA256:
- 701a9923bb2ce073338dec60bab98e8d64b22a634cc5b324576f9bd04c49c818
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