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 226
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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223.0,0.33688795791862836,0.717567162688505,0.14922399818897247,14.6288,175.886,5.537,0.25573260785075785,146734
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224.0,0.33788744193114123,0.7176529090004973,0.14929458498954773,14.4622,177.913,5.601,0.2526233968130587,147392
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225.0,0.33766902859853326,0.7175656158175208,0.1492282748222351,14.5953,176.289,5.55,0.25068013991449667,148050
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223.0,0.33688795791862836,0.717567162688505,0.14922399818897247,14.6288,175.886,5.537,0.25573260785075785,146734
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| 225 |
224.0,0.33788744193114123,0.7176529090004973,0.14929458498954773,14.4622,177.913,5.601,0.2526233968130587,147392
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| 226 |
225.0,0.33766902859853326,0.7175656158175208,0.1492282748222351,14.5953,176.289,5.55,0.25068013991449667,148050
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226.0,0.3359397226102855,0.7175191943364244,0.14919176697731018,14.306,179.855,5.662,0.2545666537116207,148708
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