metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llm-selector
results: []
widget:
- text: >-
type is summary, length is 24310, embedding is [-0.9232054948806763,
1.6205040216445923, 0.034396424889564514, 0.5600725412368774,
-0.39247140288352966, 0.2204877734184265, 1.4939937591552734,
-0.6680230498313904, 1.8432705402374268, 0.2926231622695923,
0.6553751230239868, 0.6685833930969238, -1.032821774482727,
-1.0100572109222412, 0.5711820125579834, 0.9464332461357117]
example_title: gpt-3.5-turbo-16k
- text: >-
type is summary, length is 1224, embedding is [1.4960389137268066,
0.17437873780727386, 0.2416699379682541, 1.3469676971435547,
1.3918812274932861, 0.23258954286575317, -1.1412363052368164,
-0.8899539113044739, -0.596516489982605, 1.8909876346588135,
-0.4744669497013092, 0.34388425946235657, 0.6765648722648621,
0.8031619191169739, 1.3951228857040405, -0.8443756103515625]
example_title: vertexai
- text: >-
type is summary, length is 306, embedding is [-1.1658602952957153,
-0.6684906482696533, 0.7602851986885071, 1.4131747484207153,
0.6355661749839783, -0.34228935837745667, 1.881812572479248,
-0.6283895969390869, 1.6808395385742188, 0.5185574889183044,
0.06647524237632751, 1.2375186681747437, -0.7350475192070007,
1.950120210647583, 0.5732455849647522, 0.04506298899650574]
example_title: cohere
llm-selector
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2031
- Accuracy: 0.6538
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| No log | 1.0 | 29 | 0.0769 | 2.0078 |
| No log | 2.0 | 58 | 0.0769 | 2.0159 |
| No log | 3.0 | 87 | 0.0769 | 2.0453 |
| No log | 4.0 | 116 | 0.3462 | 1.9647 |
| No log | 5.0 | 145 | 0.1154 | 2.0197 |
| No log | 6.0 | 174 | 0.1923 | 1.9459 |
| No log | 7.0 | 203 | 0.3462 | 1.7877 |
| No log | 8.0 | 232 | 0.5 | 1.6586 |
| No log | 9.0 | 261 | 0.6154 | 1.5240 |
| No log | 10.0 | 290 | 1.2031 | 0.6538 |
| No log | 11.0 | 319 | 1.2601 | 0.6538 |
| No log | 12.0 | 348 | 1.2599 | 0.6538 |
| No log | 13.0 | 377 | 1.3823 | 0.6538 |
| No log | 14.0 | 406 | 1.2636 | 0.6154 |
| No log | 15.0 | 435 | 1.3537 | 0.6154 |
| No log | 16.0 | 464 | 1.2350 | 0.6154 |
| No log | 17.0 | 493 | 1.3823 | 0.6154 |
| 0.9545 | 18.0 | 522 | 1.3141 | 0.6154 |
| 0.9545 | 19.0 | 551 | 1.4359 | 0.5769 |
| 0.9545 | 20.0 | 580 | 1.3151 | 0.6154 |
| 0.9545 | 21.0 | 609 | 1.3388 | 0.6154 |
| 0.9545 | 22.0 | 638 | 1.2909 | 0.6154 |
| 0.9545 | 23.0 | 667 | 1.5566 | 0.5769 |
| 0.9545 | 24.0 | 696 | 1.3482 | 0.6154 |
| 0.9545 | 25.0 | 725 | 1.6590 | 0.5385 |
| 0.9545 | 26.0 | 754 | 1.4044 | 0.5769 |
| 0.9545 | 27.0 | 783 | 1.4294 | 0.6154 |
| 0.9545 | 28.0 | 812 | 1.6153 | 0.5769 |
| 0.9545 | 29.0 | 841 | 1.7156 | 0.5769 |
| 0.9545 | 30.0 | 870 | 1.5582 | 0.6154 |
| 0.9545 | 31.0 | 899 | 1.5076 | 0.6154 |
| 0.9545 | 32.0 | 928 | 1.3851 | 0.6923 |
| 0.9545 | 33.0 | 957 | 1.4304 | 0.6923 |
| 0.9545 | 34.0 | 986 | 1.4978 | 0.6154 |
| 0.345 | 35.0 | 1015 | 1.4625 | 0.6538 |
| 0.345 | 36.0 | 1044 | 1.4551 | 0.6538 |
| 0.345 | 37.0 | 1073 | 1.4733 | 0.6538 |
| 0.345 | 38.0 | 1102 | 1.4840 | 0.6538 |
| 0.345 | 39.0 | 1131 | 1.4859 | 0.6538 |
| 0.345 | 40.0 | 1160 | 1.4856 | 0.6538 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3