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 84
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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81.0,0.29558450359191957,0.702740901519358,0.15511590242385864,14.5414,176.943,5.57,0.2347454333462884,53298
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| 83 |
82.0,0.3002065504084986,0.7037857430527588,0.15507493913173676,14.3334,179.511,5.651,0.23552273610571317,53956
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| 84 |
83.0,0.29934138449278447,0.7035883003316916,0.155075341463089,14.4763,177.739,5.595,0.2335794792071512,54614
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| 82 |
81.0,0.29558450359191957,0.702740901519358,0.15511590242385864,14.5414,176.943,5.57,0.2347454333462884,53298
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| 83 |
82.0,0.3002065504084986,0.7037857430527588,0.15507493913173676,14.3334,179.511,5.651,0.23552273610571317,53956
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| 84 |
83.0,0.29934138449278447,0.7035883003316916,0.155075341463089,14.4763,177.739,5.595,0.2335794792071512,54614
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| 85 |
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84.0,0.29767363518292045,0.7028010921225604,0.15480494499206543,14.417,178.469,5.618,0.23668869024485037,55272
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