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 434
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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431.0,0.347565659255218,0.72248708780294,0.1471419632434845,14.6265,175.913,5.538,0.25961912164788187,283598
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| 433 |
432.0,0.34792205857608843,0.7226940843391447,0.1470784842967987,14.4526,178.031,5.605,0.25884181888845703,284256
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| 434 |
433.0,0.34718211633020063,0.7222194600507135,0.14707998931407928,14.369,179.066,5.637,0.2592304702681695,284914
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| 432 |
431.0,0.347565659255218,0.72248708780294,0.1471419632434845,14.6265,175.913,5.538,0.25961912164788187,283598
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| 433 |
432.0,0.34792205857608843,0.7226940843391447,0.1470784842967987,14.4526,178.031,5.605,0.25884181888845703,284256
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| 434 |
433.0,0.34718211633020063,0.7222194600507135,0.14707998931407928,14.369,179.066,5.637,0.2592304702681695,284914
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| 435 |
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434.0,0.3475149541436925,0.7224787725309102,0.14712312817573547,14.6157,176.043,5.542,0.25845316750874464,285572
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| 436 |
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435.0,0.34907254106004565,0.7232164989341133,0.14716105163097382,14.4203,178.429,5.617,0.25806451612903225,286230
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