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 536
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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training_log.csv
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533.0,0.35002748303606923,0.7239275973220928,0.14673930406570435,14.2202,180.94,5.696,0.26039642440730665,350714
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| 535 |
534.0,0.349852350546487,0.7238284155715349,0.14676105976104736,14.4253,178.367,5.615,0.25961912164788187,351372
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| 536 |
535.0,0.3490988304693623,0.7234105910963323,0.1467154622077942,14.665,175.451,5.523,0.2592304702681695,352030
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| 534 |
533.0,0.35002748303606923,0.7239275973220928,0.14673930406570435,14.2202,180.94,5.696,0.26039642440730665,350714
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| 535 |
534.0,0.349852350546487,0.7238284155715349,0.14676105976104736,14.4253,178.367,5.615,0.25961912164788187,351372
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| 536 |
535.0,0.3490988304693623,0.7234105910963323,0.1467154622077942,14.665,175.451,5.523,0.2592304702681695,352030
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| 537 |
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536.0,0.34952746921548766,0.7235308703198612,0.14670132100582123,14.4054,178.613,5.623,0.25806451612903225,352688
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