Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use namesarnav/causalbench_math-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_math-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_math-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_math-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_math-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from namesarnav/causalbench_math-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 270 Bytes
-
https://huggingface.co/namesarnav/causalbench_math-roberta-base/resolve/main/eval_results.json
- Command line
-
hf download hf://namesarnav/causalbench_math-roberta-base/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/namesarnav/causalbench_math-roberta-base/resolve/main/eval_results.json
270 Bytes
| { | |
| "eval_loss": 0.26448673009872437, | |
| "eval_accuracy": 0.8953488372093024, | |
| "eval_macro_f1": 0.7952371322146137, | |
| "eval_micro_f1": 0.8953488372093024, | |
| "eval_runtime": 4.1317, | |
| "eval_samples_per_second": 624.448, | |
| "eval_steps_per_second": 19.605, | |
| "epoch": 3.0 | |
| } |