Instructions to use Ben10x/bert-base-mti881 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ben10x/bert-base-mti881 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ben10x/bert-base-mti881")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ben10x/bert-base-mti881") model = AutoModelForTokenClassification.from_pretrained("Ben10x/bert-base-mti881", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from Ben10x/bert-base-mti881: direct link, hf CLI and curl.
- Browser
- Download file 324 Bytes
-
https://huggingface.co/Ben10x/bert-base-mti881/resolve/main/predict_results.json
- Command line
-
hf download hf://Ben10x/bert-base-mti881/predict_results.json
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curl -L -o predict_results.json https://huggingface.co/Ben10x/bert-base-mti881/resolve/main/predict_results.json
324 Bytes
| { | |
| "predict_accuracy": 0.8810383719353606, | |
| "predict_f1": 0.6222756135232538, | |
| "predict_loss": 2.256601333618164, | |
| "predict_precision": 0.6286407766990292, | |
| "predict_recall": 0.6160380564050288, | |
| "predict_runtime": 5.3487, | |
| "predict_samples_per_second": 547.044, | |
| "predict_steps_per_second": 68.427 | |
| } |