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 357
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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354.0,0.34615142394910225,0.7217322366465166,0.14770740270614624,14.4791,177.704,5.594,0.2565099106101827,232932
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| 356 |
355.0,0.3439351685538685,0.7213440699870762,0.14758044481277466,14.2935,180.012,5.667,0.25767586474931986,233590
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| 357 |
356.0,0.3435791865653998,0.7211161312745147,0.14765959978103638,14.4173,178.466,5.618,0.25806451612903225,234248
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| 355 |
354.0,0.34615142394910225,0.7217322366465166,0.14770740270614624,14.4791,177.704,5.594,0.2565099106101827,232932
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| 356 |
355.0,0.3439351685538685,0.7213440699870762,0.14758044481277466,14.2935,180.012,5.667,0.25767586474931986,233590
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| 357 |
356.0,0.3435791865653998,0.7211161312745147,0.14765959978103638,14.4173,178.466,5.618,0.25806451612903225,234248
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| 358 |
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357.0,0.34358439793275297,0.7209348582794629,0.14764997363090515,14.4161,178.481,5.619,0.25806451612903225,234906
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