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 372
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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369.0,0.34498229398697067,0.7214516529952771,0.14751406013965607,14.3802,178.926,5.633,0.25767586474931986,242802
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370.0,0.34481000255002214,0.7213505461767626,0.1475074589252472,14.4475,178.093,5.607,0.25767586474931986,243460
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| 372 |
371.0,0.34528830369816116,0.721516474791584,0.14749132096767426,14.4115,178.538,5.62,0.25767586474931986,244118
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| 370 |
369.0,0.34498229398697067,0.7214516529952771,0.14751406013965607,14.3802,178.926,5.633,0.25767586474931986,242802
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| 371 |
370.0,0.34481000255002214,0.7213505461767626,0.1475074589252472,14.4475,178.093,5.607,0.25767586474931986,243460
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| 372 |
371.0,0.34528830369816116,0.721516474791584,0.14749132096767426,14.4115,178.538,5.62,0.25767586474931986,244118
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372.0,0.3442996210883431,0.7211175183933187,0.14754284918308258,14.503,177.412,5.585,0.25767586474931986,244776
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