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 293
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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290.0,0.339351970033508,0.7192991189208024,0.14831796288490295,14.404,178.631,5.623,0.2545666537116207,190820
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291.0,0.3405577289416363,0.71990441103256,0.14824265241622925,14.3105,179.799,5.66,0.25573260785075785,191478
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| 293 |
292.0,0.3392558275372256,0.7191907111177556,0.14819280803203583,14.6829,175.238,5.517,0.25573260785075785,192136
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290.0,0.339351970033508,0.7192991189208024,0.14831796288490295,14.404,178.631,5.623,0.2545666537116207,190820
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| 292 |
291.0,0.3405577289416363,0.71990441103256,0.14824265241622925,14.3105,179.799,5.66,0.25573260785075785,191478
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| 293 |
292.0,0.3392558275372256,0.7191907111177556,0.14819280803203583,14.6829,175.238,5.517,0.25573260785075785,192136
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293.0,0.33980031605067773,0.719147028050421,0.14822424948215485,14.4226,178.4,5.616,0.2549553050913331,192794
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