Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use SIRIS-Lab/specter2-fapesp-area-multiclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SIRIS-Lab/specter2-fapesp-area-multiclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SIRIS-Lab/specter2-fapesp-area-multiclass")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SIRIS-Lab/specter2-fapesp-area-multiclass") model = AutoModelForSequenceClassification.from_pretrained("SIRIS-Lab/specter2-fapesp-area-multiclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bc067760e89822059a6b36e2781b28e3b6cd4dd0b4d9d164d08b4c33ca5cf2b
- Size of remote file:
- 5.84 kB
- SHA256:
- 05d90512dde8300b3bb5d923658daf70138ebd59b85e7cfc70dbed2d5d89d2fa
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