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 128
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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125.0,0.318394773442475,0.7103983963918817,0.1521897166967392,14.6778,175.298,5.519,0.24485036921881073,82250
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126.0,0.32028803924651456,0.7103775943985996,0.15225693583488464,14.2393,180.698,5.688,0.24329576369996114,82908
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| 128 |
127.0,0.3217567786445513,0.7109777444361091,0.15220455825328827,14.5118,177.304,5.582,0.24407306645938592,83566
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| 126 |
125.0,0.318394773442475,0.7103983963918817,0.1521897166967392,14.6778,175.298,5.519,0.24485036921881073,82250
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| 127 |
126.0,0.32028803924651456,0.7103775943985996,0.15225693583488464,14.2393,180.698,5.688,0.24329576369996114,82908
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| 128 |
127.0,0.3217567786445513,0.7109777444361091,0.15220455825328827,14.5118,177.304,5.582,0.24407306645938592,83566
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128.0,0.3194517788053848,0.7107710771077108,0.15212000906467438,14.452,178.038,5.605,0.24407306645938592,84224
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