Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_v3_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_v3_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_v3_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_v3_stsb") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_v3_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from henryscheible/eval_v3_stsb: direct link, hf CLI and curl.
- Browser
- Download file 254 Bytes
-
https://huggingface.co/henryscheible/eval_v3_stsb/resolve/main/eval_results.json
- Command line
-
hf download hf://henryscheible/eval_v3_stsb/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/henryscheible/eval_v3_stsb/resolve/main/eval_results.json
254 Bytes
Invalid JSON:Unexpected token 'N', ..."d_score": NaN,
""... is not valid JSON
| { | |
| "eval_combined_score": NaN, | |
| "eval_loss": 1.3178551197052002, | |
| "eval_pearson": NaN, | |
| "eval_runtime": 4.0282, | |
| "eval_samples": 1379, | |
| "eval_samples_per_second": 342.333, | |
| "eval_spearmanr": NaN, | |
| "eval_steps_per_second": 42.947 | |
| } |