Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use cdc-hf/bert-platform-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cdc-hf/bert-platform-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cdc-hf/bert-platform-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cdc-hf/bert-platform-classifier") model = AutoModelForSequenceClassification.from_pretrained("cdc-hf/bert-platform-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0034
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- Accuracy: 0.999
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.1228 | 1.0 | 657 | 0.0123 | 0.997 |
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| 0.0172 | 2.0 | 1314 | 0.0088 | 0.999 |
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| 0.0127 | 3.0 | 1971 | 0.0051 | 0.999 |
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| 0.0068 | 4.0 | 2628 | 0.0037 | 0.999 |
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| 0.0073 | 5.0 | 3285 | 0.0048 | 0.999 |
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| 0.0088 | 6.0 | 3942 | 0.0046 | 0.999 |
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| 0.0054 | 7.0 | 4599 | 0.0021 | 0.999 |
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| 0.0048 | 8.0 | 5256 | 0.0037 | 0.999 |
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| 0.0055 | 9.0 | 5913 | 0.0037 | 0.999 |
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| 0.0048 | 10.0 | 6570 | 0.0034 | 0.999 |
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### Framework versions
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config.json
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"hidden_size": 768,
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"id2label": {
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"0": "gardening",
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"1": "furniture"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"furniture": 1,
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"gardening": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"hidden_size": 768,
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"id2label": {
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"0": "gardening",
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"1": "furniture",
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"2": "healthcare"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"furniture": 1,
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"gardening": 0,
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"healthcare": 2
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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model.safetensors
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training_args.bin
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