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 22
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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19.0,0.20890000572492995,0.6614753235820268,0.17088289558887482,14.3311,179.54,5.652,0.18655266226195102,12502
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20.0,0.21269686074800662,0.6629672285624542,0.17023713886737823,14.4262,178.357,5.615,0.18227749708511465,13160
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21.0,0.2173037125153878,0.6656912948061449,0.16946576535701752,14.278,180.208,5.673,0.18771861640108822,13818
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19.0,0.20890000572492995,0.6614753235820268,0.17088289558887482,14.3311,179.54,5.652,0.18655266226195102,12502
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20.0,0.21269686074800662,0.6629672285624542,0.17023713886737823,14.4262,178.357,5.615,0.18227749708511465,13160
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| 22 |
21.0,0.2173037125153878,0.6656912948061449,0.16946576535701752,14.278,180.208,5.673,0.18771861640108822,13818
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22.0,0.22153695778755486,0.6674666110183639,0.1687048375606537,14.5098,177.328,5.582,0.190439176059075,14476
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