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 185
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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182.0,0.3285718701738869,0.7143356818068256,0.15016813576221466,14.6184,176.012,5.541,0.2502914885347843,119756
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| 184 |
183.0,0.3296009606083294,0.7151061173533083,0.15010210871696472,14.4238,178.386,5.616,0.25301204819277107,120414
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| 185 |
184.0,0.32849290655904917,0.7148429685937188,0.15005727112293243,14.3691,179.065,5.637,0.2522347454333463,121072
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| 183 |
182.0,0.3285718701738869,0.7143356818068256,0.15016813576221466,14.6184,176.012,5.541,0.2502914885347843,119756
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| 184 |
183.0,0.3296009606083294,0.7151061173533083,0.15010210871696472,14.4238,178.386,5.616,0.25301204819277107,120414
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| 185 |
184.0,0.32849290655904917,0.7148429685937188,0.15005727112293243,14.3691,179.065,5.637,0.2522347454333463,121072
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| 186 |
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185.0,0.3297651364494541,0.7147852147852148,0.15008726716041565,14.3177,179.708,5.657,0.2510687912942091,121730
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