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 347
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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344.0,0.343411552351322,0.7208237986270023,0.14769762754440308,14.3332,179.514,5.651,0.25767586474931986,226352
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| 346 |
345.0,0.3443791340658843,0.7211060274517604,0.14769886434078217,14.3866,178.848,5.63,0.2592304702681695,227010
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| 347 |
346.0,0.34368247972809857,0.7205845802057961,0.14771637320518494,14.3219,179.655,5.656,0.2568985619898951,227668
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| 345 |
344.0,0.343411552351322,0.7208237986270023,0.14769762754440308,14.3332,179.514,5.651,0.25767586474931986,226352
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| 346 |
345.0,0.3443791340658843,0.7211060274517604,0.14769886434078217,14.3866,178.848,5.63,0.2592304702681695,227010
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| 347 |
346.0,0.34368247972809857,0.7205845802057961,0.14771637320518494,14.3219,179.655,5.656,0.2568985619898951,227668
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| 348 |
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347.0,0.34470235064196847,0.7208262985400735,0.14773933589458466,14.6066,176.153,5.545,0.25728721336960747,228326
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