Text Classification
Transformers
Safetensors
PyTorch
xlm-roberta
finance
sentiment-analysis
financial-sentiment-analysis
multilingual
financial-nlp
perspective-aware
stock-market
text-embeddings-inference
Instructions to use Kenpache/flame2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kenpache/flame2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kenpache/flame2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kenpache/flame2") model = AutoModelForSequenceClassification.from_pretrained("Kenpache/flame2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 314 Bytes
5992240 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"is_local": false,
"mask_token": "<mask>",
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
|