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 14
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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11.0,0.16772819936516337,0.6397169204374866,0.18077120184898376,14.1317,182.073,5.732,0.15934706568208318,7238
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12.0,0.17159071497969608,0.6419475655430712,0.17905373871326447,14.4557,177.992,5.603,0.16090167120093277,7896
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13.0,0.1789171582067714,0.6459455136748947,0.1774977147579193,14.4174,178.465,5.618,0.16556548775748153,8554
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11.0,0.16772819936516337,0.6397169204374866,0.18077120184898376,14.1317,182.073,5.732,0.15934706568208318,7238
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| 13 |
12.0,0.17159071497969608,0.6419475655430712,0.17905373871326447,14.4557,177.992,5.603,0.16090167120093277,7896
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| 14 |
13.0,0.1789171582067714,0.6459455136748947,0.1774977147579193,14.4174,178.465,5.618,0.16556548775748153,8554
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14.0,0.18644449382556874,0.6495780926604044,0.17618677020072937,14.3979,178.707,5.626,0.16750874465604354,9212
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