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 386
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c5f1be51acc7c8d876d4997de6ca316a93e6eec9b4acf42cb78dc56f7c7902f
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size 1109972056
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training_log.csv
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| 384 |
383.0,0.3445107619875451,0.7213767034716005,0.14741730690002441,14.4262,178.356,5.615,0.25845316750874464,252014
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| 385 |
384.0,0.34583981710892847,0.7216310293386375,0.14738813042640686,14.7067,174.954,5.508,0.25845316750874464,252672
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| 386 |
385.0,0.3447325309799067,0.7213962508080155,0.14740301668643951,14.3537,179.256,5.643,0.25806451612903225,253330
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| 384 |
383.0,0.3445107619875451,0.7213767034716005,0.14741730690002441,14.4262,178.356,5.615,0.25845316750874464,252014
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| 385 |
384.0,0.34583981710892847,0.7216310293386375,0.14738813042640686,14.7067,174.954,5.508,0.25845316750874464,252672
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| 386 |
385.0,0.3447325309799067,0.7213962508080155,0.14740301668643951,14.3537,179.256,5.643,0.25806451612903225,253330
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| 387 |
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386.0,0.34552663253334337,0.7218276632729079,0.1474054604768753,14.4354,178.243,5.611,0.25845316750874464,253988
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| 388 |
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387.0,0.34590309769203276,0.7215089081317806,0.1473512053489685,14.3046,179.872,5.663,0.25845316750874464,254646
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