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 118
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 116 |
115.0,0.3152810205292156,0.7087743942206391,0.15270662307739258,14.3344,179.498,5.651,0.24368441507967353,75670
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| 117 |
116.0,0.3172280731296564,0.7094960851234692,0.15257960557937622,14.51,177.327,5.582,0.24523902059852312,76328
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| 118 |
117.0,0.3156129334663824,0.7088798432082014,0.15256062150001526,14.538,176.984,5.572,0.24757092887679752,76986
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| 116 |
115.0,0.3152810205292156,0.7087743942206391,0.15270662307739258,14.3344,179.498,5.651,0.24368441507967353,75670
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| 117 |
116.0,0.3172280731296564,0.7094960851234692,0.15257960557937622,14.51,177.327,5.582,0.24523902059852312,76328
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| 118 |
117.0,0.3156129334663824,0.7088798432082014,0.15256062150001526,14.538,176.984,5.572,0.24757092887679752,76986
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118.0,0.3166799253041203,0.7094502841623498,0.15241198241710663,14.4732,177.777,5.597,0.2483482316362223,77644
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