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 460
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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457.0,0.3477717716246475,0.7227708033543393,0.14702320098876953,14.3996,178.685,5.625,0.2592304702681695,300706
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458.0,0.3473858655338809,0.7227299821180211,0.14702638983726501,14.4875,177.601,5.591,0.2592304702681695,301364
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| 460 |
459.0,0.3468810007776988,0.7222968602278947,0.1469578742980957,14.4254,178.366,5.615,0.26000777302759426,302022
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| 458 |
457.0,0.3477717716246475,0.7227708033543393,0.14702320098876953,14.3996,178.685,5.625,0.2592304702681695,300706
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| 459 |
458.0,0.3473858655338809,0.7227299821180211,0.14702638983726501,14.4875,177.601,5.591,0.2592304702681695,301364
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| 460 |
459.0,0.3468810007776988,0.7222968602278947,0.1469578742980957,14.4254,178.366,5.615,0.26000777302759426,302022
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| 461 |
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460.0,0.3482946064539726,0.7228150280410939,0.14695915579795837,14.3629,179.142,5.64,0.2592304702681695,302680
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461.0,0.3492261114332847,0.7229501687512409,0.1469300538301468,14.4291,178.32,5.614,0.2592304702681695,303338
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