Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 6
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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3.0,0.10309514614135397,0.5682428955343358,0.21433667838573456,16.6207,154.807,4.873,0.12786630392537893,1974
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5.0,0.12550720862659767,0.6022160664819944,0.1996934562921524,14.2268,180.855,5.693,0.1379712397979013,3290
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3.0,0.10309514614135397,0.5682428955343358,0.21433667838573456,16.6207,154.807,4.873,0.12786630392537893,1974
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| 6 |
5.0,0.12550720862659767,0.6022160664819944,0.1996934562921524,14.2268,180.855,5.693,0.1379712397979013,3290
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6.0,0.13109005516624317,0.6116456809809204,0.19493445754051208,14.4708,177.806,5.597,0.14380101049358726,3948
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