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--- |
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library_name: transformers |
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language: |
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- en |
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base_model: gokulsrinivasagan/bert_base_lda_20_v1_book |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- spearmanr |
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model-index: |
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- name: bert_base_lda_20_v1_book_stsb |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE STSB |
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type: glue |
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args: stsb |
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metrics: |
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- name: Spearmanr |
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type: spearmanr |
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value: 0.8381999392722225 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert_base_lda_20_v1_book_stsb |
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This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_20_v1_book](https://huggingface.co/gokulsrinivasagan/bert_base_lda_20_v1_book) on the GLUE STSB dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6650 |
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- Pearson: 0.8407 |
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- Spearmanr: 0.8382 |
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- Combined Score: 0.8394 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 10 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| |
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| 2.8738 | 1.0 | 23 | 2.4670 | 0.1765 | 0.1748 | 0.1756 | |
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| 1.4719 | 2.0 | 46 | 1.0280 | 0.7397 | 0.7404 | 0.7401 | |
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| 0.9801 | 3.0 | 69 | 0.8276 | 0.7956 | 0.7954 | 0.7955 | |
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| 0.783 | 4.0 | 92 | 0.7431 | 0.8197 | 0.8193 | 0.8195 | |
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| 0.5677 | 5.0 | 115 | 0.9075 | 0.8135 | 0.8152 | 0.8144 | |
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| 0.4407 | 6.0 | 138 | 0.7474 | 0.8267 | 0.8272 | 0.8269 | |
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| 0.3821 | 7.0 | 161 | 0.6753 | 0.8391 | 0.8371 | 0.8381 | |
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| 0.3036 | 8.0 | 184 | 0.8726 | 0.8246 | 0.8260 | 0.8253 | |
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| 0.269 | 9.0 | 207 | 0.7331 | 0.8311 | 0.8293 | 0.8302 | |
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| 0.2191 | 10.0 | 230 | 0.7562 | 0.8383 | 0.8368 | 0.8375 | |
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| 0.1854 | 11.0 | 253 | 0.7022 | 0.8365 | 0.8343 | 0.8354 | |
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| 0.1718 | 12.0 | 276 | 0.6650 | 0.8407 | 0.8382 | 0.8394 | |
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| 0.1685 | 13.0 | 299 | 0.7270 | 0.8350 | 0.8333 | 0.8342 | |
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| 0.1368 | 14.0 | 322 | 0.7532 | 0.8392 | 0.8376 | 0.8384 | |
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| 0.1351 | 15.0 | 345 | 0.8710 | 0.8379 | 0.8379 | 0.8379 | |
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| 0.1459 | 16.0 | 368 | 0.7801 | 0.8416 | 0.8398 | 0.8407 | |
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| 0.106 | 17.0 | 391 | 0.6833 | 0.8393 | 0.8380 | 0.8387 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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