Instructions to use namesarnav/causalbench_code-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_code-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_code-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_code-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_code-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from namesarnav/causalbench_code-bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/namesarnav/causalbench_code-bert-base-uncased/resolve/main/eval_results.json
- Command line
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hf download hf://namesarnav/causalbench_code-bert-base-uncased/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/namesarnav/causalbench_code-bert-base-uncased/resolve/main/eval_results.json
229 Bytes
| { | |
| "eval_loss": 0.5690733194351196, | |
| "eval_accuracy": 0.7080152671755725, | |
| "eval_macro_f1": 0.7029638652718904, | |
| "eval_runtime": 258.0623, | |
| "eval_samples_per_second": 6.092, | |
| "eval_steps_per_second": 0.194, | |
| "epoch": 5.0 | |
| } |