Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
text-embeddings-inference
Instructions to use tabularisai/robust-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/robust-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/robust-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/robust-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/robust-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +10 -10
config.json
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"1": "
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"initializer_range": 0.02,
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"label2id": {
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "Very Negative",
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"1": "Negative",
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"2": "Neutral",
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"3": "Positive",
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"4": "Very Positive"
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},
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"initializer_range": 0.02,
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"label2id": {
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"Very Negative":0,
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"Negative":1,
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"Neutral":2,
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"Positive":3,
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"Very Positive":5
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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