Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use MarPla/BertAbstractComp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarPla/BertAbstractComp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarPla/BertAbstractComp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MarPla/BertAbstractComp") model = AutoModelForSequenceClassification.from_pretrained("MarPla/BertAbstractComp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c797357ab0028aee55bcf3c3d465f1be1c4b5abef52882d6dd811f73be79bbce
- Size of remote file:
- 5.11 kB
- SHA256:
- 4ca8e51289f1456edacd04a1b931762b3837452ad470233a9386b2d98bbbde96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.