Instructions to use mnaylor/base-bert-finetuned-mtsamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnaylor/base-bert-finetuned-mtsamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mnaylor/base-bert-finetuned-mtsamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mnaylor/base-bert-finetuned-mtsamples") model = AutoModelForSequenceClassification.from_pretrained("mnaylor/base-bert-finetuned-mtsamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mnaylor/base-bert-finetuned-mtsamples: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mnaylor/base-bert-finetuned-mtsamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mnaylor/base-bert-finetuned-mtsamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mnaylor/base-bert-finetuned-mtsamples/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 78094b774d20eff3639807372625d63b588f4957d27ab7b9a09612bfa8db89be
- Size of remote file:
- 438 MB
- SHA256:
- 410d14c01db71605d60d1eae111711ddbf15423d83cb014800e677983a9f19ae
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