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") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 4
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658
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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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1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658
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2.0,0.09064480010203674,0.544629877304181,0.22911879420280457,14.4231,178.394,5.616,0.11232024873688301,1316
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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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4.0,0.115960350269004,0.5877596855699045,0.20582592487335205,14.3765,178.973,5.634,0.1333074232413525,2632
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