Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use eskayML/bert_interview_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eskayML/bert_interview_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eskayML/bert_interview_new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eskayML/bert_interview_new") model = AutoModelForSequenceClassification.from_pretrained("eskayML/bert_interview_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eskayML/bert_interview_new: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/eskayML/bert_interview_new/resolve/main/training_args.bin
- Command line
-
hf download hf://eskayML/bert_interview_new/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eskayML/bert_interview_new/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 86354eb74d327de24462e75e61dd9cfcda2340a29fe1a61619eb1957cd647227
- Size of remote file:
- 5.3 kB
- SHA256:
- 4be4389c28dca71b9cdc03c0481c5cb743949f9a9768a3f5fa6af0031c050bdb
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