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 149
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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146.0,0.3206190147196425,0.7116533614500176,0.15128467977046967,14.6532,175.593,5.528,0.2495141857753595,96068
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| 148 |
147.0,0.3233355919925946,0.7124575084983004,0.15131047368049622,14.3937,178.759,5.627,0.24640497473766032,96726
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| 149 |
148.0,0.32412408617406474,0.7128266746770802,0.15123490989208221,14.1787,181.469,5.713,0.2510687912942091,97384
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| 147 |
146.0,0.3206190147196425,0.7116533614500176,0.15128467977046967,14.6532,175.593,5.528,0.2495141857753595,96068
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| 148 |
147.0,0.3233355919925946,0.7124575084983004,0.15131047368049622,14.3937,178.759,5.627,0.24640497473766032,96726
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| 149 |
148.0,0.32412408617406474,0.7128266746770802,0.15123490989208221,14.1787,181.469,5.713,0.2510687912942091,97384
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| 150 |
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149.0,0.32494593789009674,0.7128346259694771,0.15125250816345215,14.7517,174.421,5.491,0.24873688301593472,98042
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