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")# pip install -U transformers accelerate # 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 131
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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128.0,0.3194517788053848,0.7107710771077108,0.15212000906467438,14.452,178.038,5.605,0.24407306645938592,84224
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129.0,0.31986515819567835,0.7104986218992734,0.15206195414066315,14.3751,178.99,5.635,0.24446171783909834,84882
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130.0,0.31923106917932226,0.7107744783306581,0.1519530564546585,14.3814,178.911,5.632,0.2467936261173727,85540
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| 129 |
128.0,0.3194517788053848,0.7107710771077108,0.15212000906467438,14.452,178.038,5.605,0.24407306645938592,84224
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| 130 |
129.0,0.31986515819567835,0.7104986218992734,0.15206195414066315,14.3751,178.99,5.635,0.24446171783909834,84882
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| 131 |
130.0,0.31923106917932226,0.7107744783306581,0.1519530564546585,14.3814,178.911,5.632,0.2467936261173727,85540
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131.0,0.3189874256760183,0.7104801083630161,0.15187682211399078,14.5517,176.818,5.566,0.24601632335794793,86198
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