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 221
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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218.0,0.3365466152536735,0.7173252279635258,0.1493675410747528,14.2552,180.495,5.682,0.2518460940536339,143444
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219.0,0.3347024105498666,0.7174172977281785,0.14928725361824036,14.3011,179.917,5.664,0.2541780023319083,144102
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220.0,0.33573160110877903,0.717355536720347,0.14926926791667938,14.5339,177.034,5.573,0.2545666537116207,144760
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218.0,0.3365466152536735,0.7173252279635258,0.1493675410747528,14.2552,180.495,5.682,0.2518460940536339,143444
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219.0,0.3347024105498666,0.7174172977281785,0.14928725361824036,14.3011,179.917,5.664,0.2541780023319083,144102
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| 221 |
220.0,0.33573160110877903,0.717355536720347,0.14926926791667938,14.5339,177.034,5.573,0.2545666537116207,144760
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221.0,0.33727455893791963,0.7170224411603722,0.14937275648117065,14.6467,175.67,5.53,0.25068013991449667,145418
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