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 272
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 270 |
269.0,0.3396909747019745,0.7186848117611999,0.14860054850578308,14.4875,177.602,5.591,0.2522347454333463,177002
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| 271 |
270.0,0.3391988016824512,0.7194594863132794,0.14851540327072144,14.3884,178.825,5.63,0.2537893509521959,177660
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| 272 |
271.0,0.3388959232523703,0.7190074441687345,0.14858926832675934,14.2969,179.969,5.666,0.2514574426739215,178318
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| 270 |
269.0,0.3396909747019745,0.7186848117611999,0.14860054850578308,14.4875,177.602,5.591,0.2522347454333463,177002
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| 271 |
270.0,0.3391988016824512,0.7194594863132794,0.14851540327072144,14.3884,178.825,5.63,0.2537893509521959,177660
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| 272 |
271.0,0.3388959232523703,0.7190074441687345,0.14858926832675934,14.2969,179.969,5.666,0.2514574426739215,178318
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272.0,0.3382172428953433,0.7186069651741294,0.14849615097045898,14.584,176.426,5.554,0.25573260785075785,178976
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