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 157
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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154.0,0.3231865683950093,0.7123698959167334,0.15099425613880157,14.5166,177.245,5.58,0.2479595802565099,101332
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| 156 |
155.0,0.32381635300795025,0.7124906132665833,0.1509193480014801,14.6092,176.121,5.544,0.2483482316362223,101990
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| 157 |
156.0,0.325067304730041,0.7130634912693251,0.15088190138339996,14.6387,175.767,5.533,0.24640497473766032,102648
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| 155 |
154.0,0.3231865683950093,0.7123698959167334,0.15099425613880157,14.5166,177.245,5.58,0.2479595802565099,101332
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| 156 |
155.0,0.32381635300795025,0.7124906132665833,0.1509193480014801,14.6092,176.121,5.544,0.2483482316362223,101990
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| 157 |
156.0,0.325067304730041,0.7130634912693251,0.15088190138339996,14.6387,175.767,5.533,0.24640497473766032,102648
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| 158 |
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157.0,0.3249519488457,0.713679268840833,0.1508830040693283,14.3458,179.355,5.646,0.2495141857753595,103306
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