Instructions to use surrey-nlp/MonoTQ-XLMV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surrey-nlp/MonoTQ-XLMV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="surrey-nlp/MonoTQ-XLMV")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("surrey-nlp/MonoTQ-XLMV") model = AutoModelForSequenceClassification.from_pretrained("surrey-nlp/MonoTQ-XLMV") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e8486888757d497d20fa3d4f16782c4581c5694b3d525c8696fd5298aba2f1f
- Size of remote file:
- 3.11 GB
- SHA256:
- a4f125e8a4585f436a06baa64d1efcd7f8c948fb5e9057f5b97723a87a9c6380
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.