Text Classification
Transformers
PyTorch
ONNX
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use AdamCodd/tinybert-emotion-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/tinybert-emotion-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdamCodd/tinybert-emotion-balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AdamCodd/tinybert-emotion-balanced") model = AutoModelForSequenceClassification.from_pretrained("AdamCodd/tinybert-emotion-balanced") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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## Intended uses & limitations
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This model is not as accurate as the [distilbert-emotion-balanced](AdamCodd/distilbert-base-uncased-finetuned-emotion-balanced) one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.
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Usage:
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```python
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## Intended uses & limitations
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This model is not as accurate as the [distilbert-emotion-balanced](https://huggingface.co/AdamCodd/distilbert-base-uncased-finetuned-emotion-balanced) one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.
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Usage:
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```python
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