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 54
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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51.0,0.27376616242553664,0.6929029630383872,0.15914605557918549,14.6599,175.513,5.525,0.22153128643606684,33558
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52.0,0.2723970041419424,0.6923860972377943,0.1589018553495407,14.5055,177.381,5.584,0.22269724057520404,34216
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| 54 |
53.0,0.2760738750100086,0.6936721728081321,0.15877537429332733,14.5029,177.413,5.585,0.22464049747376602,34874
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51.0,0.27376616242553664,0.6929029630383872,0.15914605557918549,14.6599,175.513,5.525,0.22153128643606684,33558
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| 53 |
52.0,0.2723970041419424,0.6923860972377943,0.1589018553495407,14.5055,177.381,5.584,0.22269724057520404,34216
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| 54 |
53.0,0.2760738750100086,0.6936721728081321,0.15877537429332733,14.5029,177.413,5.585,0.22464049747376602,34874
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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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