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 182
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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179.0,0.3285287877487608,0.7145572786393196,0.15015919506549835,14.5189,177.218,5.579,0.25301204819277107,117782
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180.0,0.33008184103842475,0.7146701215866056,0.15030136704444885,14.3338,179.506,5.651,0.2502914885347843,118440
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| 182 |
181.0,0.32935878687465486,0.7144568690095847,0.15013591945171356,14.5178,177.23,5.579,0.25068013991449667,119098
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| 180 |
179.0,0.3285287877487608,0.7145572786393196,0.15015919506549835,14.5189,177.218,5.579,0.25301204819277107,117782
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| 181 |
180.0,0.33008184103842475,0.7146701215866056,0.15030136704444885,14.3338,179.506,5.651,0.2502914885347843,118440
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| 182 |
181.0,0.32935878687465486,0.7144568690095847,0.15013591945171356,14.5178,177.23,5.579,0.25068013991449667,119098
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182.0,0.3285718701738869,0.7143356818068256,0.15016813576221466,14.6184,176.012,5.541,0.2502914885347843,119756
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