Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use neuralsentry/vulnfixClassification-DistilBERT-DCMB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnfixClassification-DistilBERT-DCMB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnfixClassification-DistilBERT-DCMB")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnfixClassification-DistilBERT-DCMB") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnfixClassification-DistilBERT-DCMB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from neuralsentry/vulnfixClassification-DistilBERT-DCMB: direct link, hf CLI and curl.
- Browser
- Download file 197 Bytes
-
https://huggingface.co/neuralsentry/vulnfixClassification-DistilBERT-DCMB/resolve/main/train_results.json
- Command line
-
hf download hf://neuralsentry/vulnfixClassification-DistilBERT-DCMB/train_results.json
-
curl -L -o train_results.json https://huggingface.co/neuralsentry/vulnfixClassification-DistilBERT-DCMB/resolve/main/train_results.json
197 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.04638458222828128, | |
| "train_runtime": 631.2261, | |
| "train_samples": 27992, | |
| "train_samples_per_second": 443.454, | |
| "train_steps_per_second": 1.743 | |
| } |