Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use avsolatorio/doc-topic-model_eval-02_train-03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avsolatorio/doc-topic-model_eval-02_train-03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avsolatorio/doc-topic-model_eval-02_train-03")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avsolatorio/doc-topic-model_eval-02_train-03") model = AutoModelForSequenceClassification.from_pretrained("avsolatorio/doc-topic-model_eval-02_train-03") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33a775fc14d882bd3a80c722b316110ba1cb735c89c8cebb5f35aebb3cadb37d
- Size of remote file:
- 5.24 kB
- SHA256:
- f3aa6f4713ccf2754e2cbe7c56f839b4418ffb76c7fe9e5f6aa0e6b436863e25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.