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 318
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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315.0,0.3426776380245612,0.7202185792349727,0.14798329770565033,14.4338,178.263,5.612,0.25534395647104546,207270
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316.0,0.34400473369236845,0.720852963054798,0.14807873964309692,14.3922,178.778,5.628,0.2549553050913331,207928
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| 318 |
317.0,0.3422341479547286,0.7202825027355019,0.14792263507843018,14.3714,179.036,5.636,0.2565099106101827,208586
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315.0,0.3426776380245612,0.7202185792349727,0.14798329770565033,14.4338,178.263,5.612,0.25534395647104546,207270
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| 317 |
316.0,0.34400473369236845,0.720852963054798,0.14807873964309692,14.3922,178.778,5.628,0.2549553050913331,207928
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| 318 |
317.0,0.3422341479547286,0.7202825027355019,0.14792263507843018,14.3714,179.036,5.636,0.2565099106101827,208586
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318.0,0.342250523980315,0.7202828544395199,0.1479203850030899,14.6951,175.093,5.512,0.25728721336960747,209244
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