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 321
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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318.0,0.342250523980315,0.7202828544395199,0.1479203850030899,14.6951,175.093,5.512,0.25728721336960747,209244
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319.0,0.34252663164594677,0.7201273505123869,0.14791864156723022,14.506,177.374,5.584,0.2568985619898951,209902
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| 321 |
320.0,0.3432111388120991,0.7205611661111387,0.14791163802146912,14.4528,178.028,5.604,0.25612125923047024,210560
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318.0,0.342250523980315,0.7202828544395199,0.1479203850030899,14.6951,175.093,5.512,0.25728721336960747,209244
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| 320 |
319.0,0.34252663164594677,0.7201273505123869,0.14791864156723022,14.506,177.374,5.584,0.2568985619898951,209902
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| 321 |
320.0,0.3432111388120991,0.7205611661111387,0.14791163802146912,14.4528,178.028,5.604,0.25612125923047024,210560
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321.0,0.3433217724296478,0.7205209783257108,0.14795255661010742,14.4526,178.03,5.605,0.25612125923047024,211218
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