Text Classification
Transformers
Safetensors
English
Spanish
Malayalam
sentiment-analysis
code-switching
code-mixed
data-augmentation
llm-augmentation
spanglish
spanish-english
malayalam-english
mbert
xlm-roberta
xlm-t
low-resource
Instructions to use lindazeng979/codemixed-sentiment-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lindazeng979/codemixed-sentiment-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lindazeng979/codemixed-sentiment-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lindazeng979/codemixed-sentiment-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- spanglish_MIX_mBERT_XLMT_10k
- spanglish_MIX_mBERT_XLMT_150ex-08t
- spanglish_MIX_mBERT_XLMT_150ex-small
- spanglish_MIX_mBERT_XLMT_150ex
- spanglish_MIX_mBERT_XLMT_15k
- spanglish_MIX_mBERT_XLMT_25k_5e-7
- spanglish_MIX_mBERT_XLMT_500ex
- spanglish_MIX_mBERT_XLMT_50ex
- spanglish_MIX_mBERT_XLMT_50k_5e-7
- spanglish_MIX_mBERT_XLMT_5k
- spanglish_MIX_mBERT_XLMT_stages
- spanglish_MIX_tokenizer_MBERT_15k
- spanglish_MIX_tokenizer_XLMT_10k
- spanglish_MIX_tokenizer_XLMT_15-shot
- spanglish_MIX_tokenizer_XLMT_150ex-08t
- spanglish_MIX_tokenizer_XLMT_150ex-small
- spanglish_MIX_tokenizer_XLMT_150ex
- spanglish_MIX_tokenizer_XLMT_500ex
- spanglish_MIX_tokenizer_XLMT_50ex
- spanglish_MIX_tokenizer_XLMT_50k_5e-7
- spanglish_MIX_tokenizer_XLMT_5k
- spanglish_MIX_tokenizer_XLMT_stages
- spanglish_NCM_mBERT_XLMT_LINCE
- spanglish_SCM_mBERT_XLMT-15k
- spanglish_SCM_tokenizer_XLMT-15k