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 11
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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8.0,0.14152951052414994,0.6225964149918523,0.18794220685958862,14.3092,179.814,5.661,0.14457831325301204,5264
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9.0,0.14935181006664486,0.6283051582141309,0.18516190350055695,14.5423,176.932,5.57,0.15001943256898562,5922
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| 11 |
10.0,0.15952444743230132,0.635693929128354,0.18292535841464996,14.5123,177.298,5.581,0.15235134084726001,6580
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8.0,0.14152951052414994,0.6225964149918523,0.18794220685958862,14.3092,179.814,5.661,0.14457831325301204,5264
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| 10 |
9.0,0.14935181006664486,0.6283051582141309,0.18516190350055695,14.5423,176.932,5.57,0.15001943256898562,5922
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| 11 |
10.0,0.15952444743230132,0.635693929128354,0.18292535841464996,14.5123,177.298,5.581,0.15235134084726001,6580
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11.0,0.16772819936516337,0.6397169204374866,0.18077120184898376,14.1317,182.073,5.732,0.15934706568208318,7238
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