Instructions to use Jennny/eng_rm_1e5_700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jennny/eng_rm_1e5_700 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jennny/eng_rm_1e5_700")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jennny/eng_rm_1e5_700") model = AutoModelForSequenceClassification.from_pretrained("Jennny/eng_rm_1e5_700", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c92fa50fbba9054fde950dcecfcb43b6ad1c5962f4d49ea4122787cd780c720
- Size of remote file:
- 5.37 kB
- SHA256:
- aef4bf873a75c3381619bc484de3173613a391aaac55a07206c963fc0d6140ff
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