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 540
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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537.0,0.35024052826119156,0.7237037771388917,0.14674507081508636,14.4939,177.522,5.589,0.2592304702681695,353346
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538.0,0.34911757148135625,0.7231526114396462,0.14664146304130554,14.1758,181.506,5.714,0.25884181888845703,354004
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| 540 |
539.0,0.3491093021024498,0.7234654890090806,0.1467103511095047,14.3286,179.571,5.653,0.25961912164788187,354662
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| 538 |
537.0,0.35024052826119156,0.7237037771388917,0.14674507081508636,14.4939,177.522,5.589,0.2592304702681695,353346
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| 539 |
538.0,0.34911757148135625,0.7231526114396462,0.14664146304130554,14.1758,181.506,5.714,0.25884181888845703,354004
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| 540 |
539.0,0.3491093021024498,0.7234654890090806,0.1467103511095047,14.3286,179.571,5.653,0.25961912164788187,354662
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540.0,0.34985666412733357,0.7236195862479535,0.1467028707265854,14.4244,178.378,5.615,0.25845316750874464,355320
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