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 396
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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393.0,0.3462069207148712,0.7219846872824898,0.14735470712184906,14.4791,177.704,5.594,0.25884181888845703,258594
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| 395 |
394.0,0.34753902896290934,0.7222387320455671,0.1474439650774002,14.4339,178.261,5.612,0.25767586474931986,259252
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| 396 |
395.0,0.3478929430109067,0.7218306363546113,0.1473572701215744,14.5023,177.421,5.585,0.25767586474931986,259910
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| 394 |
393.0,0.3462069207148712,0.7219846872824898,0.14735470712184906,14.4791,177.704,5.594,0.25884181888845703,258594
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| 395 |
394.0,0.34753902896290934,0.7222387320455671,0.1474439650774002,14.4339,178.261,5.612,0.25767586474931986,259252
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| 396 |
395.0,0.3478929430109067,0.7218306363546113,0.1473572701215744,14.5023,177.421,5.585,0.25767586474931986,259910
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| 397 |
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396.0,0.3473536014101492,0.7218999454608558,0.14736206829547882,14.6563,175.556,5.527,0.25728721336960747,260568
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| 398 |
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397.0,0.34597569218721386,0.722018075280564,0.14733925461769104,14.6097,176.116,5.544,0.25845316750874464,261226
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