|
--- |
|
license: bigscience-bloom-rail-1.0 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bloom-560m-finetuned-unnatural-instructions-6k-steps |
|
results: [] |
|
widget: |
|
- text: "<s>You will be provided with a short text that you should read. After reading the text, answer the question 'Who is telling the story in first person?'\nText: I went to the store and bought some milk.\nThe output should be one of the two options: 'I' or 'Not I'.\nOutput:" |
|
- text: "<s>You are given the title and opening paragraph of an article. Your task is to find the main idea of the article using context clues from the text.\nText: The first confirmed case of Zika in Africa has been found in Uganda, health officials said on Tuesday, more than 4,000 miles away from Brazil where a major outbreak began last year.\nAblood test carried out at a private hospital showed that a five-year-old girl who had returned from Mozambique three weeks ago had contracted Zika, which can cause birth defects in babies born to infected mothers.\nOutput:" |
|
- text: "<s>We have given you a list of sentences. Your task is to go through this list and find all the unique words used in these sentences and store them in a dictionary.\nThis is an example sentence., That is another example sentence., Here is an interesting sentence!\nThe output should be a Python dictionary with keys as the unique words and values as the number of times that word occurs in the text.\nOutput:\n" |
|
- text: "<s>You will given a set of two or more words. Output the shortest word in the set. If there is a tie for the shortest word, output all tied words in alphabetical order, separated by space.\nWords: defenestrate, circumambulate, excommunication.\nThe input will be lowercase and only contain alphanumeric characters and spaces.\nOutput:\n" |
|
|
|
inference: |
|
parameters: |
|
max_length: 150 |
|
|
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# bloom-560m-finetuned-unnatural-instructions-6k-steps |
|
|
|
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.4037 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 6000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 1.6758 | 0.32 | 1000 | 1.6487 | |
|
| 1.512 | 0.63 | 2000 | 1.5360 | |
|
| 1.4658 | 0.95 | 3000 | 1.4580 | |
|
| 1.0616 | 1.26 | 4000 | 1.4510 | |
|
| 1.0291 | 1.58 | 5000 | 1.4199 | |
|
| 0.9906 | 1.89 | 6000 | 1.4037 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.25.1 |
|
- Pytorch 1.13.0+cu116 |
|
- Datasets 2.8.0 |
|
- Tokenizers 0.13.2 |
|
|
