Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use avsolatorio/doc-topic-model_eval-03_train-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avsolatorio/doc-topic-model_eval-03_train-01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avsolatorio/doc-topic-model_eval-03_train-01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avsolatorio/doc-topic-model_eval-03_train-01") model = AutoModelForSequenceClassification.from_pretrained("avsolatorio/doc-topic-model_eval-03_train-01") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c083e6adf70ab9659f75a87a88c7ba748b8b317b32a922002bfca732a9f39e34
- Size of remote file:
- 5.24 kB
- SHA256:
- 84861f878e0d1001145daa935d0e2fc4aeed2d155f7d316ba66fe609e0bb986e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.