Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
sentiment-analysis
text-classification
generic
sentiment-classification
text-embeddings-inference
Instructions to use numind/NuSentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuSentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="numind/NuSentiment")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("numind/NuSentiment") model = AutoModel.from_pretrained("numind/NuSentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- text-classification
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- generic
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- sentiment-classification
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---
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## Model
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Base version of e5-v2 finetunned on an annotated subset of C4
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## Usage
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- text-classification
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- generic
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- sentiment-classification
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datasets:
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- Numind/C4_sentiment-analysis
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## Model
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Base version of [e5-v2](https://huggingface.co/intfloat/e5-base-v2) finetunned on an annotated subset of [C4](https://huggingface.co/datasets/Numind/C4_sentiment-analysis). 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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