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 101
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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98.0,0.3071877006465677,0.705421293272371,0.15386764705181122,14.3583,179.2,5.641,0.23785464438398757,64484
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99.0,0.3079052536354255,0.7059711253081141,0.1537395715713501,14.5489,176.852,5.567,0.24368441507967353,65142
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| 101 |
100.0,0.30977598913567506,0.7077340842803983,0.15365947782993317,14.5179,177.23,5.579,0.24251846094053633,65800
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| 99 |
98.0,0.3071877006465677,0.705421293272371,0.15386764705181122,14.3583,179.2,5.641,0.23785464438398757,64484
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| 100 |
99.0,0.3079052536354255,0.7059711253081141,0.1537395715713501,14.5489,176.852,5.567,0.24368441507967353,65142
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| 101 |
100.0,0.30977598913567506,0.7077340842803983,0.15365947782993317,14.5179,177.23,5.579,0.24251846094053633,65800
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101.0,0.3087163899246343,0.7067048941200141,0.1535627394914627,14.303,179.892,5.663,0.24368441507967353,66458
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