初始化项目,由ModelHub XC社区提供模型
Model: postbot/distilgpt2-emailgen-V2 Source: Original Platform
This commit is contained in:
144
README.md
Normal file
144
README.md
Normal file
@@ -0,0 +1,144 @@
|
||||
---
|
||||
license: apache-2.0
|
||||
tags:
|
||||
- generated_from_trainer
|
||||
- distilgpt2
|
||||
- email generation
|
||||
- email
|
||||
datasets:
|
||||
- aeslc
|
||||
- postbot/multi-emails-100k
|
||||
|
||||
widget:
|
||||
- text: "Good Morning Professor Beans,
|
||||
|
||||
Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
|
||||
example_title: "email to prof"
|
||||
- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
|
||||
example_title: "newsletter"
|
||||
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours"
|
||||
example_title: "office hours"
|
||||
- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because"
|
||||
example_title: "festival"
|
||||
- text: "Good Morning Harold,\n\nI was wondering when the next"
|
||||
example_title: "event"
|
||||
- text: "URGENT - I need the TPS reports"
|
||||
example_title: "URGENT"
|
||||
- text: "Hi Archibald,\n\nI hope this email finds you extremely well."
|
||||
example_title: "emails that find you"
|
||||
- text: "Hello there.\n\nI just wanted to reach out and check in to"
|
||||
example_title: "checking in"
|
||||
- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us"
|
||||
example_title: "work well"
|
||||
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
|
||||
example_title: "catch up"
|
||||
- text: "I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and"
|
||||
example_title: "grocery"
|
||||
parameters:
|
||||
min_length: 4
|
||||
max_length: 128
|
||||
length_penalty: 0.8
|
||||
no_repeat_ngram_size: 2
|
||||
do_sample: False
|
||||
num_beams: 8
|
||||
early_stopping: True
|
||||
repetition_penalty: 5.5
|
||||
---
|
||||
|
||||
|
||||
# distilgpt2-emailgen: V2
|
||||
|
||||
|
||||
[](https://colab.research.google.com/gist/pszemraj/d1c2d88b6120cca4ca7df078ea1d1e50/scratchpad.ipynb)
|
||||
|
||||
Why write the rest of your email when you can generate it?
|
||||
|
||||
```python
|
||||
from transformers import pipeline
|
||||
|
||||
model_tag = "postbot/distilgpt2-emailgen-V2"
|
||||
generator = pipeline(
|
||||
'text-generation',
|
||||
model=model_tag,
|
||||
)
|
||||
|
||||
prompt = """
|
||||
Hello,
|
||||
|
||||
Following up on the bubblegum shipment."""
|
||||
|
||||
result = generator(
|
||||
prompt,
|
||||
max_length=64,
|
||||
do_sample=False,
|
||||
early_stopping=True,
|
||||
) # generate
|
||||
print(result[0]['generated_text'])
|
||||
```
|
||||
|
||||
## Model description
|
||||
|
||||
This model is a fine-tuned version of `distilgpt2` on the postbot/multi-emails-100k dataset.
|
||||
It achieves the following results on the evaluation set:
|
||||
- Loss: 1.9126
|
||||
|
||||
|
||||
## Intended uses & limitations
|
||||
|
||||
More information needed
|
||||
|
||||
## Training and evaluation data
|
||||
|
||||
More information needed
|
||||
|
||||
## Training procedure
|
||||
|
||||
### Training hyperparameters (run 1/2)
|
||||
|
||||
TODO
|
||||
|
||||
### Training hyperparameters (run 2/2)
|
||||
|
||||
The following hyperparameters were used during training:
|
||||
- learning_rate: 0.0006
|
||||
- train_batch_size: 16
|
||||
- eval_batch_size: 16
|
||||
- seed: 42
|
||||
- distributed_type: multi-GPU
|
||||
- gradient_accumulation_steps: 8
|
||||
- total_train_batch_size: 128
|
||||
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
||||
- lr_scheduler_type: cosine
|
||||
- lr_scheduler_warmup_ratio: 0.01
|
||||
- num_epochs: 4
|
||||
|
||||
### Training results
|
||||
|
||||
| Training Loss | Epoch | Step | Validation Loss |
|
||||
|:-------------:|:-----:|:----:|:---------------:|
|
||||
| 1.9045 | 1.0 | 789 | 2.0006 |
|
||||
| 1.8115 | 2.0 | 1578 | 1.9557 |
|
||||
| 1.8501 | 3.0 | 2367 | 1.9110 |
|
||||
| 1.7376 | 4.0 | 3156 | 1.9126 |
|
||||
|
||||
|
||||
### Framework versions
|
||||
|
||||
- Transformers 4.22.2
|
||||
- Pytorch 1.10.0+cu113
|
||||
- Datasets 2.5.1
|
||||
- Tokenizers 0.12.1
|
||||
|
||||
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
||||
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen-V2)
|
||||
|
||||
| Metric | Value |
|
||||
|-----------------------|---------------------------|
|
||||
| Avg. | 24.59 |
|
||||
| ARC (25-shot) | 20.99 |
|
||||
| HellaSwag (10-shot) | 26.78 |
|
||||
| MMLU (5-shot) | 25.53 |
|
||||
| TruthfulQA (0-shot) | 46.51 |
|
||||
| Winogrande (5-shot) | 52.01 |
|
||||
| GSM8K (5-shot) | 0.0 |
|
||||
| DROP (3-shot) | 0.31 |
|
||||
Reference in New Issue
Block a user