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 39
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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36.0,0.2537988900571717,0.6831474533586884,0.1626574844121933,14.676,175.32,5.519,0.20870579090555771,23688
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37.0,0.2554963745112012,0.6834336264979685,0.16228148341178894,14.5382,176.982,5.572,0.2129809560823941,24346
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38.0,0.2557336132810033,0.6840377513336069,0.16205258667469025,14.4671,177.851,5.599,0.2106490478041197,25004
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36.0,0.2537988900571717,0.6831474533586884,0.1626574844121933,14.676,175.32,5.519,0.20870579090555771,23688
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| 38 |
37.0,0.2554963745112012,0.6834336264979685,0.16228148341178894,14.5382,176.982,5.572,0.2129809560823941,24346
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| 39 |
38.0,0.2557336132810033,0.6840377513336069,0.16205258667469025,14.4671,177.851,5.599,0.2106490478041197,25004
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39.0,0.257067977223277,0.6854093987276831,0.1617140918970108,14.4867,177.611,5.591,0.2129809560823941,25662
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