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 299
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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296.0,0.3417731656704175,0.7202345458159412,0.1482512503862381,14.692,175.129,5.513,0.25573260785075785,194768
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297.0,0.3394640329623906,0.7193829310773824,0.14821989834308624,14.6002,176.23,5.548,0.25534395647104546,195426
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| 299 |
298.0,0.34131067808149373,0.7198887895938835,0.1482653021812439,14.3793,178.938,5.633,0.2526233968130587,196084
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| 297 |
296.0,0.3417731656704175,0.7202345458159412,0.1482512503862381,14.692,175.129,5.513,0.25573260785075785,194768
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| 298 |
297.0,0.3394640329623906,0.7193829310773824,0.14821989834308624,14.6002,176.23,5.548,0.25534395647104546,195426
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| 299 |
298.0,0.34131067808149373,0.7198887895938835,0.1482653021812439,14.3793,178.938,5.633,0.2526233968130587,196084
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299.0,0.34205628065586946,0.7201190771520715,0.14823143184185028,14.3905,178.799,5.629,0.2541780023319083,196742
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