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 420
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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417.0,0.34675744344335757,0.7226139636508094,0.14723117649555206,14.3354,179.485,5.65,0.2592304702681695,274386
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418.0,0.3464682371697305,0.7225812866660037,0.14720655977725983,14.401,178.668,5.625,0.26000777302759426,275044
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| 420 |
419.0,0.34628945923764703,0.722255369928401,0.14717763662338257,14.6114,176.095,5.544,0.2592304702681695,275702
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| 418 |
417.0,0.34675744344335757,0.7226139636508094,0.14723117649555206,14.3354,179.485,5.65,0.2592304702681695,274386
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| 419 |
418.0,0.3464682371697305,0.7225812866660037,0.14720655977725983,14.401,178.668,5.625,0.26000777302759426,275044
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| 420 |
419.0,0.34628945923764703,0.722255369928401,0.14717763662338257,14.6114,176.095,5.544,0.2592304702681695,275702
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420.0,0.3463248144933797,0.7222608522896593,0.14721375703811646,14.2811,180.168,5.672,0.25884181888845703,276360
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421.0,0.34661189850202345,0.7224043715846995,0.14718474447727203,14.6395,175.758,5.533,0.2592304702681695,277018
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