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 494
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 sha256:3769d511b3819825a30e4fbf5cb26fed92bc6b6d1ccae2f49de5fd52e5d04fab
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training_log.csv
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| 492 |
491.0,0.35005327936776626,0.7233325068187454,0.14685863256454468,14.2442,180.634,5.687,0.25961912164788187,323078
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| 493 |
492.0,0.34911543642544435,0.7227993439037725,0.14678499102592468,14.3412,179.413,5.648,0.26039642440730665,323736
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| 494 |
493.0,0.35029868574566725,0.7237774364564237,0.14685066044330597,14.5773,176.507,5.557,0.26039642440730665,324394
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| 492 |
491.0,0.35005327936776626,0.7233325068187454,0.14685863256454468,14.2442,180.634,5.687,0.25961912164788187,323078
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| 493 |
492.0,0.34911543642544435,0.7227993439037725,0.14678499102592468,14.3412,179.413,5.648,0.26039642440730665,323736
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| 494 |
493.0,0.35029868574566725,0.7237774364564237,0.14685066044330597,14.5773,176.507,5.557,0.26039642440730665,324394
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| 495 |
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494.0,0.3496544754668595,0.7232710651828299,0.14678241312503815,14.3182,179.701,5.657,0.2615623785464438,325052
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| 496 |
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495.0,0.3497476971884754,0.7235901509134234,0.14680397510528564,14.4248,178.374,5.615,0.26039642440730665,325710
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