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 262
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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259.0,0.33729798292618807,0.7179231650804724,0.1486329436302185,14.3136,179.759,5.659,0.2549553050913331,170422
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260.0,0.33893056458302695,0.7189081885856079,0.14866165816783905,14.5167,177.244,5.58,0.2526233968130587,171080
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261.0,0.3385420085352499,0.7186224743704589,0.1486891806125641,14.4351,178.246,5.611,0.2545666537116207,171738
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259.0,0.33729798292618807,0.7179231650804724,0.1486329436302185,14.3136,179.759,5.659,0.2549553050913331,170422
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260.0,0.33893056458302695,0.7189081885856079,0.14866165816783905,14.5167,177.244,5.58,0.2526233968130587,171080
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| 262 |
261.0,0.3385420085352499,0.7186224743704589,0.1486891806125641,14.4351,178.246,5.611,0.2545666537116207,171738
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262.0,0.33720678413952226,0.718552126439647,0.14863678812980652,14.4551,178.0,5.604,0.2565099106101827,172396
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