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")# pip install -U transformers accelerate # 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 97
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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94.0,0.306437189293798,0.7054384464456407,0.15409868955612183,14.3044,179.874,5.663,0.24212980956082394,61852
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| 96 |
95.0,0.30842502732088334,0.7069416498993963,0.15394039452075958,14.5781,176.498,5.556,0.24174115818111155,62510
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| 97 |
96.0,0.30677401099772905,0.7058230908724562,0.15395987033843994,14.4534,178.021,5.604,0.24096385542168675,63168
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| 95 |
94.0,0.306437189293798,0.7054384464456407,0.15409868955612183,14.3044,179.874,5.663,0.24212980956082394,61852
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| 96 |
95.0,0.30842502732088334,0.7069416498993963,0.15394039452075958,14.5781,176.498,5.556,0.24174115818111155,62510
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| 97 |
96.0,0.30677401099772905,0.7058230908724562,0.15395987033843994,14.4534,178.021,5.604,0.24096385542168675,63168
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| 98 |
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97.0,0.3062978738084595,0.7056150272672187,0.15383179485797882,14.4115,178.538,5.621,0.24135250680139914,63826
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