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 17
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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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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15.0,0.19204255281350513,0.6531367263886687,0.1748652458190918,14.451,178.05,5.605,0.16673144189661873,9870
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16.0,0.19736083782585614,0.6549719961957096,0.17370301485061646,14.1778,181.481,5.713,0.1737271667314419,10528
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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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15.0,0.19204255281350513,0.6531367263886687,0.1748652458190918,14.451,178.05,5.605,0.16673144189661873,9870
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| 17 |
16.0,0.19736083782585614,0.6549719961957096,0.17370301485061646,14.1778,181.481,5.713,0.1737271667314419,10528
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17.0,0.20190792482249356,0.6579917816879148,0.17270566523075104,14.2818,180.16,5.672,0.17839098328799066,11186
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