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 266
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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263.0,0.3379620807660971,0.7187468920934859,0.14866986870765686,14.5229,177.169,5.577,0.2537893509521959,173054
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264.0,0.33859750961867996,0.7189659458115834,0.14860865473747253,14.6771,175.307,5.519,0.25534395647104546,173712
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| 266 |
265.0,0.3380213841878019,0.7184524106297053,0.14852477610111237,14.3861,178.853,5.63,0.2568985619898951,174370
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263.0,0.3379620807660971,0.7187468920934859,0.14866986870765686,14.5229,177.169,5.577,0.2537893509521959,173054
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| 265 |
264.0,0.33859750961867996,0.7189659458115834,0.14860865473747253,14.6771,175.307,5.519,0.25534395647104546,173712
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| 266 |
265.0,0.3380213841878019,0.7184524106297053,0.14852477610111237,14.3861,178.853,5.63,0.2568985619898951,174370
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266.0,0.33838819508010437,0.718986083499006,0.1485602855682373,14.6589,175.525,5.526,0.2526233968130587,175028
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