Instructions to use mgreenbe/bertlet-base-uncased-for-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgreenbe/bertlet-base-uncased-for-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mgreenbe/bertlet-base-uncased-for-sequence-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mgreenbe/bertlet-base-uncased-for-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("mgreenbe/bertlet-base-uncased-for-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b7c3a5ac3cb17e97b396ec6bce9df7a6b5caad685bd717a935f648a95e0b071
- Size of remote file:
- 2.93 kB
- SHA256:
- 86a1706668c123b31b10e0b0c4bff0f1404710b79ac15315ae5213e895e761b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.