Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use MiMe-MeMo/MeMo-BERT-SA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiMe-MeMo/MeMo-BERT-SA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiMe-MeMo/MeMo-BERT-SA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiMe-MeMo/MeMo-BERT-SA") model = AutoModelForSequenceClassification.from_pretrained("MiMe-MeMo/MeMo-BERT-SA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d44228d673f34241ce8c6e3222c8a7ef4f5ed196fbb29c2977ff2d3263c163a2
- Size of remote file:
- 5.05 kB
- SHA256:
- da86dc89d10f94d39f46e507ca77b7128719fa99412c91f51aa8deae6c220a59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.