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")# 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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://iarfmoose/bert-base-cased-qa-evaluator/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 5c4ee19e5e57e3cb4ea731f69b89e92d82f05f34690b4310413b8d82559af6f7
- Size of remote file:
- 433 MB
- SHA256:
- b6c43e27473f1e763ef3fd87c266ca985380444c43e73fba51c5c7dd98e03289
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