Feature Extraction
Transformers
PyTorch
Safetensors
xlm-roberta
sentiment-analysis
text-classification
generic
sentiment-classification
multilingual
text-embeddings-inference
Instructions to use numind/NuSentiment-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuSentiment-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="numind/NuSentiment-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("numind/NuSentiment-multilingual") model = AutoModel.from_pretrained("numind/NuSentiment-multilingual") - Notebooks
- Google Colab
- Kaggle
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Base version of e5-multilingual finetunned on an annotated subset of mC4 (multilingual C4). This model provide generic embedding for sentiment analysis. Embeddings can be used out of the box or fine tune on specific datasets.
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## Usage
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Base version of e5-multilingual finetunned on an annotated subset of mC4 (multilingual C4). This model provide generic embedding for sentiment analysis. Embeddings can be used out of the box or fine tune on specific datasets.
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Blog post: https://www.numind.ai/blog/creating-task-specific-foundation-models-with-gpt-4
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## Usage
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