Instructions to use engindemir/bart_tr_dependencyparsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engindemir/bart_tr_dependencyparsing with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("engindemir/bart_tr_dependencyparsing") model = AutoModelForSeq2SeqLM.from_pretrained("engindemir/bart_tr_dependencyparsing") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +68 -0
- generation_config.json +13 -0
README.md
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---
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license: apache-2.0
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base_model: facebook/bart-base
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tags:
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- generated_from_trainer
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model-index:
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- name: bart_tr_dependencyparsing
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results: []
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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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# bart_tr_dependencyparsing
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0190
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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: 4
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:-----:|:---------------:|
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| 0.3959 | 0.2440 | 1000 | 0.0680 |
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| 0.0739 | 0.4879 | 2000 | 0.0496 |
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| 0.0593 | 0.7319 | 3000 | 0.0397 |
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| 0.0506 | 0.9758 | 4000 | 0.0351 |
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| 0.0432 | 1.2198 | 5000 | 0.0308 |
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| 0.0397 | 1.4638 | 6000 | 0.0275 |
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| 0.0362 | 1.7077 | 7000 | 0.0253 |
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| 0.0347 | 1.9517 | 8000 | 0.0231 |
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| 0.0305 | 2.1957 | 9000 | 0.0222 |
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| 0.0288 | 2.4396 | 10000 | 0.0206 |
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| 0.0272 | 2.6836 | 11000 | 0.0196 |
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| 0.0264 | 2.9275 | 12000 | 0.0190 |
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### Framework versions
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- Transformers 4.42.4
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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"early_stopping": true,
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"eos_token_id": 2,
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"forced_bos_token_id": 0,
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"forced_eos_token_id": 2,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"pad_token_id": 1,
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"transformers_version": "4.42.4"
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}
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