Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use namesarnav/causalbench_code-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_code-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_code-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_code-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_code-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fine-tuned on causal classification
Browse files- eval_results.json +10 -0
eval_results.json
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{
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"eval_loss": 0.6042093634605408,
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"eval_accuracy": 0.6711195928753181,
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"eval_macro_f1": 0.6462256697550816,
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"eval_micro_f1": 0.6711195928753181,
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"eval_runtime": 2.8082,
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"eval_samples_per_second": 559.793,
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"eval_steps_per_second": 17.805,
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"epoch": 3.0
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}
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