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 274
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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271.0,0.3388959232523703,0.7190074441687345,0.14858926832675934,14.2969,179.969,5.666,0.2514574426739215,178318
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| 273 |
272.0,0.3382172428953433,0.7186069651741294,0.14849615097045898,14.584,176.426,5.554,0.25573260785075785,178976
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| 274 |
273.0,0.3384151447670933,0.7188059701492537,0.14848896861076355,14.2155,181.0,5.698,0.2545666537116207,179634
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271.0,0.3388959232523703,0.7190074441687345,0.14858926832675934,14.2969,179.969,5.666,0.2514574426739215,178318
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| 273 |
272.0,0.3382172428953433,0.7186069651741294,0.14849615097045898,14.584,176.426,5.554,0.25573260785075785,178976
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| 274 |
273.0,0.3384151447670933,0.7188059701492537,0.14848896861076355,14.2155,181.0,5.698,0.2545666537116207,179634
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| 275 |
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274.0,0.3378617501517251,0.7189913788807495,0.14844299852848053,14.5287,177.098,5.575,0.25534395647104546,180292
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