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RefalMachine
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llm_model_m_unigram_hm_1e_v0.1

Text Generation
Transformers
PyTorch
mistral
Generated from Trainer
text-generation-inference
Model card Files
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Community
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Commit
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Files changed (16) hide show
  1. README.md +174 -0
  2. added_tokens.json +5 -0
  3. all_results.json +15 -0
  4. config.json +25 -0
  5. eval_results.json +10 -0
  6. generation_config.json +6 -0
  7. pytorch_model-00001-of-00002.bin +3 -0
  8. pytorch_model-00002-of-00002.bin +3 -0
  9. pytorch_model.bin.index.json +298 -0
  10. special_tokens_map.json +10 -0
  11. tokenizer.json +0 -0
  12. tokenizer.model +3 -0
  13. tokenizer_config.json +46 -0
  14. train_results.json +8 -0
  15. trainer_state.json +0 -0
  16. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ base_model: mistral_rulm_unigram_init_20_10_23
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: mistral7b_darulm_unigram_1e_20_10_23
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+ results: []
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+ ---
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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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+
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+ # mistral7b_darulm_unigram_1e_20_10_23
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+
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+ This model is a fine-tuned version of [mistral_rulm_unigram_init_20_10_23](https://huggingface.co/mistral_rulm_unigram_init_20_10_23) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.7017
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+ - Accuracy: 0.4706
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 6
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+ - eval_batch_size: 6
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 10
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 240
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+ - total_eval_batch_size: 60
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+ - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 1.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|
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+ | 3.1631 | 0.01 | 1000 | 3.1804 | 0.4182 |
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+ | 3.1124 | 0.02 | 2000 | 3.1065 | 0.4272 |
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+ | 3.0757 | 0.03 | 3000 | 3.0894 | 0.4288 |
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+ | 3.0488 | 0.04 | 4000 | 3.0630 | 0.4319 |
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+ | 3.0403 | 0.05 | 5000 | 3.0423 | 0.4336 |
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+ | 3.0172 | 0.05 | 6000 | 3.0384 | 0.4343 |
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+ | 3.0102 | 0.06 | 7000 | 3.0267 | 0.4360 |
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+ | 2.9888 | 0.07 | 8000 | 3.0190 | 0.4361 |
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+ | 3.0024 | 0.08 | 9000 | 3.0040 | 0.4385 |
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+ | 2.9948 | 0.09 | 10000 | 3.0058 | 0.4378 |
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+ | 2.9774 | 0.1 | 11000 | 2.9962 | 0.4389 |
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+ | 2.9818 | 0.11 | 12000 | 2.9964 | 0.4390 |
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+ | 2.9771 | 0.12 | 13000 | 2.9913 | 0.4396 |
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+ | 2.9786 | 0.13 | 14000 | 2.9915 | 0.4391 |
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+ | 2.9866 | 0.14 | 15000 | 2.9924 | 0.4394 |
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+ | 2.9751 | 0.14 | 16000 | 2.9918 | 0.4389 |
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+ | 2.9702 | 0.15 | 17000 | 2.9926 | 0.4393 |
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+ | 2.9695 | 0.16 | 18000 | 2.9816 | 0.4401 |
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+ | 2.9615 | 0.17 | 19000 | 2.9826 | 0.4402 |
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+ | 2.9609 | 0.18 | 20000 | 2.9791 | 0.4406 |
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+ | 2.9607 | 0.19 | 21000 | 2.9684 | 0.4416 |
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+ | 2.9533 | 0.2 | 22000 | 2.9677 | 0.4422 |
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+ | 2.9513 | 0.21 | 23000 | 2.9676 | 0.4421 |
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+ | 2.9563 | 0.22 | 24000 | 2.9610 | 0.4429 |
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+ | 2.9466 | 0.23 | 25000 | 2.9627 | 0.4424 |
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+ | 2.9431 | 0.24 | 26000 | 2.9590 | 0.4424 |
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+ | 2.9412 | 0.24 | 27000 | 2.9525 | 0.4436 |
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+ | 2.9299 | 0.25 | 28000 | 2.9504 | 0.4435 |
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+ | 2.9332 | 0.26 | 29000 | 2.9486 | 0.4435 |
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+ | 2.9255 | 0.27 | 30000 | 2.9425 | 0.4442 |
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+ | 2.9242 | 0.28 | 31000 | 2.9459 | 0.4434 |
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+ | 2.9242 | 0.29 | 32000 | 2.9378 | 0.4445 |
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+ | 2.9267 | 0.3 | 33000 | 2.9316 | 0.4453 |
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+ | 2.9151 | 0.31 | 34000 | 2.9315 | 0.4454 |
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+ | 2.9105 | 0.32 | 35000 | 2.9286 | 0.4456 |
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+ | 2.9053 | 0.33 | 36000 | 2.9242 | 0.4457 |
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+ | 2.9023 | 0.33 | 37000 | 2.9195 | 0.4466 |
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+ | 2.8946 | 0.34 | 38000 | 2.9177 | 0.4468 |
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+ | 2.9037 | 0.35 | 39000 | 2.9147 | 0.4470 |
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+ | 2.8893 | 0.36 | 40000 | 2.9130 | 0.4468 |
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+ | 2.8891 | 0.37 | 41000 | 2.9055 | 0.4481 |
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+ | 2.8851 | 0.38 | 42000 | 2.9017 | 0.4485 |
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+ | 2.8909 | 0.39 | 43000 | 2.9011 | 0.4483 |
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+ | 2.896 | 0.4 | 44000 | 2.9061 | 0.4479 |
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+ | 2.8918 | 0.41 | 45000 | 2.9043 | 0.4479 |
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+ | 2.8847 | 0.42 | 46000 | 2.8954 | 0.4490 |
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+ | 2.8749 | 0.42 | 47000 | 2.8912 | 0.4494 |
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+ | 2.8832 | 0.43 | 48000 | 2.8912 | 0.4496 |
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+ | 2.8745 | 0.44 | 49000 | 2.8853 | 0.4500 |
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+ | 2.8717 | 0.45 | 50000 | 2.8834 | 0.4502 |
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+ | 2.8659 | 0.46 | 51000 | 2.8831 | 0.4503 |
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+ | 2.865 | 0.47 | 52000 | 2.8784 | 0.4505 |
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+ | 2.8575 | 0.48 | 53000 | 2.8763 | 0.4508 |
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+ | 2.8571 | 0.49 | 54000 | 2.8741 | 0.4513 |
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+ | 2.8554 | 0.5 | 55000 | 2.8704 | 0.4514 |
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+ | 2.8521 | 0.52 | 57000 | 2.8618 | 0.4525 |
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+ | 2.8398 | 0.52 | 58000 | 2.8600 | 0.4522 |
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+ | 2.8398 | 0.53 | 59000 | 2.8576 | 0.4528 |
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+ | 2.837 | 0.54 | 60000 | 2.8536 | 0.4528 |
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+ | 2.837 | 0.55 | 61000 | 2.8519 | 0.4535 |
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+ | 2.8427 | 0.56 | 62000 | 2.8493 | 0.4536 |
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+ | 2.8365 | 0.57 | 63000 | 2.8468 | 0.4541 |
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+ | 2.8289 | 0.59 | 65000 | 2.8388 | 0.4546 |
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+ | 2.8166 | 0.6 | 66000 | 2.8346 | 0.4547 |
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+ | 2.8171 | 0.61 | 67000 | 2.8294 | 0.4558 |
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+ | 2.8184 | 0.61 | 68000 | 2.8269 | 0.4556 |
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+ | 2.8102 | 0.62 | 69000 | 2.8243 | 0.4563 |
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+ | 2.8153 | 0.63 | 70000 | 2.8211 | 0.4564 |
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+ | 2.8035 | 0.64 | 71000 | 2.8185 | 0.4569 |
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+ | 2.8042 | 0.65 | 72000 | 2.8206 | 0.4569 |
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+ | 2.7984 | 0.66 | 73000 | 2.8138 | 0.4574 |
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+ | 2.7962 | 0.68 | 75000 | 2.8056 | 0.4584 |
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+ | 2.7937 | 0.69 | 76000 | 2.8068 | 0.4582 |
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+ | 2.7853 | 0.7 | 77000 | 2.8011 | 0.4588 |
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+ | 2.7658 | 0.75 | 83000 | 2.7798 | 0.4610 |
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+ | 2.7551 | 0.8 | 89000 | 2.7607 | 0.4634 |
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+ | 2.7397 | 0.81 | 90000 | 2.7579 | 0.4637 |
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+ | 2.7357 | 0.82 | 91000 | 2.7580 | 0.4636 |
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+ | 2.7452 | 0.83 | 92000 | 2.7517 | 0.4643 |
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+ | 2.7418 | 0.84 | 93000 | 2.7533 | 0.4641 |
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+ | 2.7379 | 0.85 | 94000 | 2.7481 | 0.4647 |
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+ | 2.7308 | 0.86 | 95000 | 2.7460 | 0.4654 |
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+ | 2.727 | 0.87 | 96000 | 2.7408 | 0.4655 |
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+ | 2.7282 | 0.88 | 97000 | 2.7351 | 0.4664 |
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+ | 2.7136 | 0.9 | 99000 | 2.7251 | 0.4673 |
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+ | 2.7108 | 0.9 | 100000 | 2.7208 | 0.4679 |
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+ | 2.7051 | 0.91 | 101000 | 2.7192 | 0.4681 |
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+ | 2.7013 | 0.92 | 102000 | 2.7151 | 0.4687 |
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+ | 2.6996 | 0.93 | 103000 | 2.7129 | 0.4689 |
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+ | 2.6898 | 0.94 | 104000 | 2.7084 | 0.4694 |
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+ | 2.6901 | 0.99 | 110000 | 2.7006 | 0.4707 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.14.1
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+ "rms_norm_eps": 1e-05,
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