Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use bmdavis/my-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmdavis/my-language-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bmdavis/my-language-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bmdavis/my-language-model") model = AutoModelForSequenceClassification.from_pretrained("bmdavis/my-language-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download results/checkpoint-500/training_args.bin from bmdavis/my-language-model: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/bmdavis/my-language-model/resolve/2c2a85d24728988efe54c858a1223726461afec6/results/checkpoint-500/training_args.bin
- Command line
-
hf download hf://bmdavis/my-language-model@2c2a85d24728988efe54c858a1223726461afec6/results/checkpoint-500/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bmdavis/my-language-model/resolve/2c2a85d24728988efe54c858a1223726461afec6/results/checkpoint-500/training_args.bin
5.33 kB
- Xet hash:
- 4f98eb309568a6df36e06dafa62413da02ae9608cfc07a6e6d653094c45e385d
- Size of remote file:
- 5.33 kB
- SHA256:
- 0bbea30fa528a706a75824b0dc6a7c088554c90e711ee73c1b8e434dd2b4f6d4
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