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 254
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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251.0,0.33871422919252936,0.7191726332537789,0.14881345629692078,14.2343,180.76,5.69,0.2541780023319083,165158
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252.0,0.3376118743872579,0.7184475705876484,0.14870189130306244,14.4184,178.453,5.618,0.25612125923047024,165816
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| 254 |
253.0,0.33917901406043693,0.7185974698675167,0.14871463179588318,14.4122,178.53,5.62,0.2549553050913331,166474
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251.0,0.33871422919252936,0.7191726332537789,0.14881345629692078,14.2343,180.76,5.69,0.2541780023319083,165158
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| 253 |
252.0,0.3376118743872579,0.7184475705876484,0.14870189130306244,14.4184,178.453,5.618,0.25612125923047024,165816
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| 254 |
253.0,0.33917901406043693,0.7185974698675167,0.14871463179588318,14.4122,178.53,5.62,0.2549553050913331,166474
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254.0,0.337421069715233,0.7182650218861918,0.14878371357917786,14.731,174.666,5.499,0.25068013991449667,167132
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