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---
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license: apache-2.0
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language:
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- en
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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pipeline_tag: text-classification
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library_name: peft
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tags:
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- regression
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- story-point-estimation
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- software-engineering
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datasets:
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- mesos
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metrics:
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- mae
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- mdae
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model-index:
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- name: DeepSeek-R1-Distill-Qwen-1.5B-story-point-estimation
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results:
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- task:
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type: regression
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name: Story Point Estimation
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dataset:
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name: mesos Dataset
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type: mesos
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split: test
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metrics:
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- type: mae
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value: 1.438
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name: Mean Absolute Error (MAE)
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- type: mdae
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value: 1.17
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name: Median Absolute Error (MdAE)
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---
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# DeepSeek R1 Qwen Story Point Estimator - mesos |
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This model is fine-tuned on issue descriptions from mesos and tested on mesos for story point estimation. |
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## Model Details |
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- Base Model: DeepSeek R1 Distill Qwen 1.5B |
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- Training Project: mesos |
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- Test Project: mesos |
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- Task: Story Point Estimation (Regression) |
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- Architecture: PEFT (LoRA) |
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- Tokenizer: DeepSeek BPE Tokenizer |
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- Input: Issue titles |
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- Output: Story point estimation (continuous value) |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification |
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from peft import PeftConfig, PeftModel |
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from transformers import AutoTokenizer |
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# Load peft config model |
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config = PeftConfig.from_pretrained("DEVCamiloSepulveda/1-DeepSeekR1SP-mesos") |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("DEVCamiloSepulveda/1-DeepSeekR1SP-mesos") |
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base_model = AutoModelForSequenceClassification.from_pretrained( |
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config.base_model_name_or_path, |
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num_labels=1, |
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torch_dtype=torch.float16, |
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device_map='auto' |
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) |
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model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/1-DeepSeekR1SP-mesos") |
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# Prepare input text |
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text = "Your issue description here" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=20, padding="max_length") |
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# Get prediction |
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outputs = model(**inputs) |
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story_points = outputs.logits.item() |
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``` |
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## Training Details |
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- Fine-tuning method: LoRA (Low-Rank Adaptation) |
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- Sequence length: 20 tokens |
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- Best training epoch: 3 / 20 epochs |
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- Batch size: 32 |
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- Training time: 269.757 seconds |
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- Mean Absolute Error (MAE): 1.438 |
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- Median Absolute Error (MdAE): 1.170 |
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### Framework versions |
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- PEFT 0.14.0 |
