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 74
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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71.0,0.29322997203558887,0.7005352994647005,0.1562827080488205,14.5302,177.079,5.575,0.232413525068014,46718
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72.0,0.2934365672581728,0.7005666869054847,0.15607893466949463,14.3003,179.927,5.664,0.23396813058686358,47376
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| 74 |
73.0,0.29494853105828284,0.7012842552330872,0.1560181975364685,14.4637,177.894,5.6,0.2335794792071512,48034
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| 72 |
71.0,0.29322997203558887,0.7005352994647005,0.1562827080488205,14.5302,177.079,5.575,0.232413525068014,46718
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| 73 |
72.0,0.2934365672581728,0.7005666869054847,0.15607893466949463,14.3003,179.927,5.664,0.23396813058686358,47376
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| 74 |
73.0,0.29494853105828284,0.7012842552330872,0.1560181975364685,14.4637,177.894,5.6,0.2335794792071512,48034
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74.0,0.2932262485923011,0.701192400970089,0.1559084802865982,14.4747,177.759,5.596,0.23280217644772638,48692
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