File size: 2,309 Bytes
13f0519 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
license: apache-2.0
language:
- en
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-classification
library_name: peft
tags:
- regression
- story-point-estimation
- software-engineering
datasets:
- mesos
metrics:
- mae
- mdae
model-index:
- name: DeepSeek-R1-Distill-Qwen-1.5B-story-point-estimation
results:
- task:
type: regression
name: Story Point Estimation
dataset:
name: mesos Dataset
type: mesos
split: test
metrics:
- type: mae
value: 1.438
name: Mean Absolute Error (MAE)
- type: mdae
value: 1.17
name: Median Absolute Error (MdAE)
---
# DeepSeek R1 Qwen Story Point Estimator - mesos
This model is fine-tuned on issue descriptions from mesos and tested on mesos for story point estimation.
## Model Details
- Base Model: DeepSeek R1 Distill Qwen 1.5B
- Training Project: mesos
- Test Project: mesos
- Task: Story Point Estimation (Regression)
- Architecture: PEFT (LoRA)
- Tokenizer: DeepSeek BPE Tokenizer
- Input: Issue titles
- Output: Story point estimation (continuous value)
## Usage
```python
from transformers import AutoModelForSequenceClassification
from peft import PeftConfig, PeftModel
from transformers import AutoTokenizer
# Load peft config model
config = PeftConfig.from_pretrained("DEVCamiloSepulveda/1-DeepSeekR1SP-mesos")
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/1-DeepSeekR1SP-mesos")
base_model = AutoModelForSequenceClassification.from_pretrained(
config.base_model_name_or_path,
num_labels=1,
torch_dtype=torch.float16,
device_map='auto'
)
model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/1-DeepSeekR1SP-mesos")
# Prepare input text
text = "Your issue description here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=20, padding="max_length")
# Get prediction
outputs = model(**inputs)
story_points = outputs.logits.item()
```
## Training Details
- Fine-tuning method: LoRA (Low-Rank Adaptation)
- Sequence length: 20 tokens
- Best training epoch: 3 / 20 epochs
- Batch size: 32
- Training time: 269.757 seconds
- Mean Absolute Error (MAE): 1.438
- Median Absolute Error (MdAE): 1.170
### Framework versions
- PEFT 0.14.0 |