Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use albertmartinez/openalex-topic-classification-title-abstract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albertmartinez/openalex-topic-classification-title-abstract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="albertmartinez/openalex-topic-classification-title-abstract")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("albertmartinez/openalex-topic-classification-title-abstract") model = AutoModelForSequenceClassification.from_pretrained("albertmartinez/openalex-topic-classification-title-abstract") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 478bc3a083dc0bee948ee0386da828b538965248e2f9bb450ade75ec9dcfaa65
- Size of remote file:
- 5.43 kB
- SHA256:
- 79a504cf8170baad34e76c3aec5ae84cf1372d6790c579670c8490ad649b49f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.