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 88
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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training_log.csv
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85.0,0.29968949601240014,0.7035226528246736,0.15484844148159027,14.6822,175.247,5.517,0.23668869024485037,55930
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| 87 |
86.0,0.30034132101084304,0.7034232050893668,0.15475152432918549,14.4263,178.355,5.615,0.23746599300427518,56588
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| 88 |
87.0,0.3043316164427821,0.7040692989524577,0.15465013682842255,14.3346,179.495,5.651,0.23902059852312477,57246
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| 86 |
85.0,0.29968949601240014,0.7035226528246736,0.15484844148159027,14.6822,175.247,5.517,0.23668869024485037,55930
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| 87 |
86.0,0.30034132101084304,0.7034232050893668,0.15475152432918549,14.4263,178.355,5.615,0.23746599300427518,56588
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| 88 |
87.0,0.3043316164427821,0.7040692989524577,0.15465013682842255,14.3346,179.495,5.651,0.23902059852312477,57246
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88.0,0.3051466543134685,0.7051558813193434,0.15466587245464325,14.5714,176.578,5.559,0.23435678196657597,57904
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