Token Classification
Transformers
Safetensors
English
xlm-roberta
cyber-threat-intelligence
cyner
named-entity-recognition
hallucination-evaluation
Eval Results (legacy)
Instructions to use Ricky-dev/halluvision-cyner-xlmr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ricky-dev/halluvision-cyner-xlmr-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ricky-dev/halluvision-cyner-xlmr-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ricky-dev/halluvision-cyner-xlmr-base") model = AutoModelForTokenClassification.from_pretrained("Ricky-dev/halluvision-cyner-xlmr-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download parameter.json from Ricky-dev/halluvision-cyner-xlmr-base: direct link, hf CLI and curl.
- Browser
- Download file 251 Bytes
-
https://huggingface.co/Ricky-dev/halluvision-cyner-xlmr-base/resolve/main/parameter.json
- Command line
-
hf download hf://Ricky-dev/halluvision-cyner-xlmr-base/parameter.json
-
curl -L -o parameter.json https://huggingface.co/Ricky-dev/halluvision-cyner-xlmr-base/resolve/main/parameter.json
251 Bytes
| {"dataset": "dataset_new", "transformers_model": "xlm-roberta-base", "random_seed": 1, "lr": 5e-06, "epochs": 30, "warmup_step": 0, "weight_decay": 1e-07, "batch_size": 32, "max_seq_length": 128, "fp16": false, "max_grad_norm": 1, "lower_case": false} |