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 57
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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54.0,0.2770279507641395,0.6935656426715574,0.15849138796329498,14.31,179.805,5.66,0.22425184609405363,35532
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55.0,0.27859331843906077,0.6948738812042311,0.1582983434200287,14.4687,177.832,5.598,0.22308589195491643,36190
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| 57 |
56.0,0.27915931265494287,0.6946308724832215,0.1581912636756897,14.4373,178.218,5.61,0.22502914885347844,36848
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| 55 |
54.0,0.2770279507641395,0.6935656426715574,0.15849138796329498,14.31,179.805,5.66,0.22425184609405363,35532
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| 56 |
55.0,0.27859331843906077,0.6948738812042311,0.1582983434200287,14.4687,177.832,5.598,0.22308589195491643,36190
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| 57 |
56.0,0.27915931265494287,0.6946308724832215,0.1581912636756897,14.4373,178.218,5.61,0.22502914885347844,36848
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57.0,0.28322400288888616,0.6952207371405428,0.15820305049419403,14.4564,177.984,5.603,0.22425184609405363,37506
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