Instructions to use joelniklaus/bert-base-uncased-sem_eval_2010_task_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelniklaus/bert-base-uncased-sem_eval_2010_task_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joelniklaus/bert-base-uncased-sem_eval_2010_task_8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joelniklaus/bert-base-uncased-sem_eval_2010_task_8") model = AutoModelForSequenceClassification.from_pretrained("joelniklaus/bert-base-uncased-sem_eval_2010_task_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from joelniklaus/bert-base-uncased-sem_eval_2010_task_8: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/joelniklaus/bert-base-uncased-sem_eval_2010_task_8/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://joelniklaus/bert-base-uncased-sem_eval_2010_task_8/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/joelniklaus/bert-base-uncased-sem_eval_2010_task_8/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 081522971a80e72aa19c09e965ec8ed8327ed9f5b78a475a7e03d9ed6b2596a9
- Size of remote file:
- 438 MB
- SHA256:
- ea8ef87486bc6b98bad9f34dbdb2d7033a50dd365f7c989ff4f6efe700300c16
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