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 543
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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540.0,0.34985666412733357,0.7236195862479535,0.1467028707265854,14.4244,178.378,5.615,0.25845316750874464,355320
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| 542 |
541.0,0.3504510423843426,0.7239908757314292,0.1467178910970688,14.5662,176.642,5.561,0.25884181888845703,355978
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| 543 |
542.0,0.35007817319828094,0.7236574602072693,0.14671221375465393,14.6095,176.118,5.544,0.2592304702681695,356636
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| 541 |
540.0,0.34985666412733357,0.7236195862479535,0.1467028707265854,14.4244,178.378,5.615,0.25845316750874464,355320
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| 542 |
541.0,0.3504510423843426,0.7239908757314292,0.1467178910970688,14.5662,176.642,5.561,0.25884181888845703,355978
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| 543 |
542.0,0.35007817319828094,0.7236574602072693,0.14671221375465393,14.6095,176.118,5.544,0.2592304702681695,356636
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| 544 |
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543.0,0.3501438529578002,0.7237991266375546,0.14667768776416779,14.3272,179.588,5.654,0.2592304702681695,357294
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