Text Classification
Transformers
Safetensors
PyTorch
eurobert
fine-tuned
sequence-classification
binary-classification
geopolitics
multilingual
custom_code
Instructions to use Durrani95/eurobert-geopolitical-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Durrani95/eurobert-geopolitical-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Durrani95/eurobert-geopolitical-binary", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Durrani95/eurobert-geopolitical-binary", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("Durrani95/eurobert-geopolitical-binary", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 582 Bytes
d9a5d91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"bos_token": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|end_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"mask_token": {
"content": "<|mask|>",
"lstrip": true,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
|