SlyGoblin
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mistral_ATSscore_generation

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 ---
 library_name: peft
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 ---
-# Model Card for Model ID
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 ### Framework versions
-- PEFT 0.8.2

 ---
+license: apache-2.0
 library_name: peft
+tags:
+  - trl
+  - sft
+  - generated_from_trainer
+  - natural-language-processing
+  - chatbot
+  - resume-evaluation
 base_model: mistralai/Mistral-7B-Instruct-v0.2
+model-index:
+  - name: mistral_instruct_generation
+    results:
+      - task:
+          name: Resume Scoring
+          type: text-generation
+        metrics:
+          - name: Loss
+            type: Lower is better
+            value: 1.6300
 ---
+<!-- 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. -->
+# mistral_instruct_generation (Resume ATS score generation based on Job description)
+This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
+This model is a specialized chatbot designed to automate the evaluation of resumes by providing an ATS (Applicant Tracking System) score based on a given job description. It is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), utilizing a custom dataset tailored for the nuances of job descriptions and resume content.
+## Model description
+The `mistral_instruct_generation` model employs advanced NLP techniques to understand and compare the content of resumes against job descriptions. It aims to support applicants by offering an automated, preliminary assessment of candidate suitability, streamlining the initial stages of the hiring process.
+## Intended uses & limitations
+This model is intended for use in HR technology platforms and recruitment software, providing an automated way to score resumes against job descriptions. It is designed to enhance, not replace, human decision-making processes in recruitment.
+Limitations include potential biases in training data and the need for regular updates to adapt to evolving job market requirements. Users should be aware of these limitations and use the model's output as one of several tools in a comprehensive recruitment process.
+## Training and evaluation data
+More information needed
+## Training procedure
+The model was trained on a Custom dataset comprising pairs of resumes and job descriptions across various industries. This dataset was curated to cover a broad spectrum of job roles, experience levels, and skills. The specifics of the dataset composition can provide further insights into the model's capabilities and potential biases.
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_steps: 0.03
+- num_epochs: 4
+### Training results
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.8804        | 0.17  | 20   | 1.8834          |
+| 1.8364        | 0.34  | 40   | 1.8631          |
+| 1.8363        | 0.51  | 60   | 1.8547          |
+| 1.8312        | 0.68  | 80   | 1.8298          |
+| 1.7648        | 0.85  | 100  | 1.8102          |
+| 1.6197        | 1.02  | 120  | 1.7888          |
+| 1.6869        | 1.19  | 140  | 1.7887          |
+| 1.5637        | 1.36  | 160  | 1.7672          |
+| 1.6921        | 1.53  | 180  | 1.7476          |
+| 1.5883        | 1.69  | 200  | 1.7305          |
+| 1.5235        | 1.86  | 220  | 1.7099          |
+| 1.6134        | 2.03  | 240  | 1.7045          |
+| 1.4006        | 2.2   | 260  | 1.7191          |
+| 1.5571        | 2.37  | 280  | 1.6963          |
+| 1.3889        | 2.54  | 300  | 1.6869          |
+| 1.4278        | 2.71  | 320  | 1.6658          |
+| 1.3868        | 2.88  | 340  | 1.6592          |
+| 1.1515        | 3.05  | 360  | 1.6576          |
+| 1.2761        | 3.22  | 380  | 1.6553          |
+| 1.1679        | 3.39  | 400  | 1.6439          |
+| 1.3966        | 3.56  | 420  | 1.6301          |
+| 1.2536        | 3.73  | 440  | 1.6200          |
+| 1.262         | 3.9   | 460  | 1.6300          |
 ### Framework versions
+- PEFT 0.8.2
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.0
+- Tokenizers 0.15.2