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 63
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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60.0,0.2863074822058199,0.6975186481960725,0.15761305391788483,14.4019,178.657,5.624,0.22852701127089,39480
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61.0,0.2873034632574655,0.6974815414180237,0.15752507746219635,14.5185,177.222,5.579,0.2289156626506024,40138
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| 63 |
62.0,0.2879660474143668,0.6978723404255319,0.15729965269565582,14.491,177.558,5.59,0.2300816167897396,40796
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| 61 |
60.0,0.2863074822058199,0.6975186481960725,0.15761305391788483,14.4019,178.657,5.624,0.22852701127089,39480
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| 62 |
61.0,0.2873034632574655,0.6974815414180237,0.15752507746219635,14.5185,177.222,5.579,0.2289156626506024,40138
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| 63 |
62.0,0.2879660474143668,0.6978723404255319,0.15729965269565582,14.491,177.558,5.59,0.2300816167897396,40796
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63.0,0.28852850368768906,0.6976579493145835,0.15732619166374207,14.5226,177.172,5.578,0.22852701127089,41454
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