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 289
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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286.0,0.34165383757785633,0.7192250372578242,0.14837491512298584,14.4262,178.356,5.615,0.2541780023319083,188188
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287.0,0.34010605171603087,0.7194144301150227,0.1483272761106491,14.3252,179.614,5.654,0.2568985619898951,188846
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| 289 |
288.0,0.3400609278709284,0.7199086712661935,0.1483636051416397,14.5189,177.218,5.579,0.2522347454333463,189504
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| 287 |
286.0,0.34165383757785633,0.7192250372578242,0.14837491512298584,14.4262,178.356,5.615,0.2541780023319083,188188
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| 288 |
287.0,0.34010605171603087,0.7194144301150227,0.1483272761106491,14.3252,179.614,5.654,0.2568985619898951,188846
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| 289 |
288.0,0.3400609278709284,0.7199086712661935,0.1483636051416397,14.5189,177.218,5.579,0.2522347454333463,189504
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289.0,0.3406516340593571,0.7198766721368541,0.1482987254858017,14.1595,181.715,5.721,0.25573260785075785,190162
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