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 341
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 339 |
338.0,0.3428453776566097,0.7206437512418041,0.14777711033821106,14.638,175.776,5.534,0.2568985619898951,222404
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| 340 |
339.0,0.3418502660981613,0.7203706471379465,0.14776265621185303,14.1889,181.339,5.709,0.25767586474931986,223062
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| 341 |
340.0,0.34257700192031143,0.7205172842576474,0.14774300158023834,14.4976,177.478,5.587,0.25767586474931986,223720
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| 339 |
338.0,0.3428453776566097,0.7206437512418041,0.14777711033821106,14.638,175.776,5.534,0.2568985619898951,222404
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| 340 |
339.0,0.3418502660981613,0.7203706471379465,0.14776265621185303,14.1889,181.339,5.709,0.25767586474931986,223062
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| 341 |
340.0,0.34257700192031143,0.7205172842576474,0.14774300158023834,14.4976,177.478,5.587,0.25767586474931986,223720
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| 342 |
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341.0,0.34450850300467206,0.7211762952660077,0.14772874116897583,14.5084,177.346,5.583,0.25612125923047024,224378
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