Text Classification
Transformers
Safetensors
English
roberta
emotion
emotions
multi-class-classification
multi-label-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use minuva/MiniLMv2-goemotions-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-goemotions-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-goemotions-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-goemotions-v2") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-goemotions-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minuva/MiniLMv2-goemotions-v2: direct link, hf CLI and curl.
- Browser
- Download file 4.35 kB
-
https://huggingface.co/minuva/MiniLMv2-goemotions-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://minuva/MiniLMv2-goemotions-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minuva/MiniLMv2-goemotions-v2/resolve/main/training_args.bin
4.35 kB
- Xet hash:
- 702a48d4f1f180382d74c684c860552efc7e632f23a4727513bf86627c3ae478
- Size of remote file:
- 4.35 kB
- SHA256:
- b8f7958c1a1f7ea76d4a99dcadd98950e2816855bfe15183a7f5e434b38cc797
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