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mnlin task

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  1. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/args.json +3 -0
  2. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/logfile.log +23 -0
  3. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/mnli_bert-base-uncased_validation_loss.png +0 -0
  4. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/README.md +202 -0
  5. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/adapter_config.json +3 -0
  6. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/adapter_model.safetensors +3 -0
  7. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/all_results.json +3 -0
  8. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/all_results_val.json +3 -0
  9. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/eval_res.json +3 -0
  10. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/gpu_stats.json +3 -0
  11. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/special_tokens_map.json +3 -0
  12. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/tokenizer.json +3 -0
  13. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/tokenizer_config.json +3 -0
  14. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/val_res.json +3 -0
  15. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/vocab.txt +0 -0
  16. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/README.md +202 -0
  17. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/adapter_config.json +3 -0
  18. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/adapter_model.safetensors +3 -0
  19. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/all_results.json +3 -0
  20. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/all_results_val.json +3 -0
  21. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/eval_res.json +3 -0
  22. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/gpu_stats.json +3 -0
  23. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/special_tokens_map.json +3 -0
  24. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/tokenizer.json +3 -0
  25. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/tokenizer_config.json +3 -0
  26. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/val_res.json +3 -0
  27. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_11780/vocab.txt +0 -0
  28. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/README.md +202 -0
  29. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/adapter_config.json +3 -0
  30. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/adapter_model.safetensors +3 -0
  31. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/all_results.json +3 -0
  32. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/all_results_val.json +3 -0
  33. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/eval_res.json +3 -0
  34. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/gpu_stats.json +3 -0
  35. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/special_tokens_map.json +3 -0
  36. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/tokenizer.json +3 -0
  37. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/tokenizer_config.json +3 -0
  38. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/val_res.json +3 -0
  39. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_17671/vocab.txt +0 -0
  40. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/README.md +202 -0
  41. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/adapter_config.json +3 -0
  42. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/adapter_model.safetensors +3 -0
  43. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/all_results.json +3 -0
  44. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/all_results_val.json +3 -0
  45. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/eval_res.json +3 -0
  46. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/gpu_stats.json +3 -0
  47. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/special_tokens_map.json +3 -0
  48. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/tokenizer.json +3 -0
  49. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/tokenizer_config.json +3 -0
  50. outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_23562/val_res.json +3 -0
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+ 05/28/2024 01:19:00 - INFO - __main__ - Sample 217445 of the training set: {'input_ids': [101, 1005, 9680, 4263, 6240, 1010, 1005, 1045, 2409, 2014, 1012, 102, 1045, 2409, 2014, 2009, 2001, 2013, 1037, 9680, 4263, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 0}.
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+ 05/28/2024 01:19:00 - INFO - __main__ - Sample 151437 of the training set: {'input_ids': [101, 7910, 2292, 1005, 1055, 2156, 2085, 1045, 2228, 2057, 1005, 2128, 2006, 2193, 2809, 2030, 7891, 2057, 1005, 2128, 2471, 2000, 2193, 2702, 1045, 2228, 2138, 1045, 1005, 2310, 2042, 2182, 2702, 2086, 102, 1045, 2031, 2042, 2182, 2146, 2438, 2000, 2113, 2256, 2597, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}.
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+ 05/28/2024 01:19:00 - INFO - __main__ - Sample 150837 of the training set: {'input_ids': [101, 1999, 1011, 2839, 4933, 1012, 102, 3772, 1037, 2112, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 0}.
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+ 05/28/2024 01:19:01 - INFO - __main__ - ***** Running training *****
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+ 05/28/2024 01:19:01 - INFO - __main__ - Num examples = 314161
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+ 05/28/2024 01:19:01 - INFO - __main__ - Num Epochs = 3
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+ 05/28/2024 01:19:01 - INFO - __main__ - Instantaneous batch size per device = 32
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+ 05/28/2024 01:19:01 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 32
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+ 05/28/2024 01:19:01 - INFO - __main__ - Gradient Accumulation steps = 1
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+ 05/28/2024 01:19:01 - INFO - __main__ - Total optimization steps = 29454
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+ 05/28/2024 01:19:51 - INFO - __main__ - epoch 0: {'accuracy': 0.3169638308711156}
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+ 05/28/2024 01:27:04 - INFO - __main__ - epoch 0: {'accuracy': 0.36479036426834394}
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+ 05/28/2024 02:02:42 - INFO - __main__ - epoch 0: {'accuracy': 0.7597554763117677}
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+ 05/28/2024 02:10:00 - INFO - __main__ - epoch 0: {'accuracy': 0.7555162271934404}
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+ 05/28/2024 02:45:39 - INFO - __main__ - epoch 1: {'accuracy': 0.7810494141619969}
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+ 05/28/2024 02:52:57 - INFO - __main__ - epoch 1: {'accuracy': 0.7754548579722692}
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+ 05/28/2024 03:28:35 - INFO - __main__ - epoch 1: {'accuracy': 0.7865511971472237}
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+ 05/28/2024 03:35:53 - INFO - __main__ - epoch 1: {'accuracy': 0.782699481799315}
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+ 05/28/2024 04:11:21 - INFO - __main__ - epoch 2: {'accuracy': 0.7938869077941926}
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+ 05/28/2024 04:18:38 - INFO - __main__ - epoch 2: {'accuracy': 0.7905552513973594}
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+ 05/28/2024 04:54:17 - INFO - __main__ - epoch 2: {'accuracy': 0.7981660723382578}
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+ 05/28/2024 05:01:35 - INFO - __main__ - epoch 2: {'accuracy': 0.7916502209037318}
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+ 05/28/2024 05:01:36 - INFO - __main__ - ***** Completed training *****
outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/mnli_bert-base-uncased_validation_loss.png ADDED
outputs/mnli/bert-base-uncased_loratrain_val_8_16_0.1_0.0001_65/step_0/README.md ADDED
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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53
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+
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65
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
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+ ## How to Get Started with the Model
71
+
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+ Use the code below to get started with the model.
73
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+ [More Information Needed]
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+ ## Training Details
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+ ### Training Data
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ [More Information Needed]
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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145
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