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 200
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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197.0,0.3302113385460995,0.7159113595528049,0.1497601568698883,14.2587,180.452,5.681,0.25301204819277107,129626
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197.0,0.3302113385460995,0.7159113595528049,0.1497601568698883,14.2587,180.452,5.681,0.25301204819277107,129626
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| 200 |
199.0,0.33196910615637276,0.7161084529505582,0.14981813728809357,14.1547,181.777,5.722,0.25068013991449667,130942
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200.0,0.33139635080315744,0.7160814046288907,0.1497674584388733,14.6029,176.198,5.547,0.2514574426739215,131600
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