Continued LoRA training on career guidance dataset (adapter update)

README.md

@@ -1,48 +1,206 @@
 ---
-license: other
-datasets:
-- datastax/linkedin_job_listings
-- Sachin21112004/DreamFlow-AI-Data
-- aida-ugent/JobHop
-- xanderios/linkedin-job-postings
-base_model:
-- Sachin21112004/carrerflow-ai
-pipeline_tag: question-answering
-language:
-- en
-metrics:
-- accuracy
+base_model: Sachin21112004/carrerflow-ai
+library_name: peft
+tags:
+- base_model:adapter:Sachin21112004/carrerflow-ai
+- lora
+- transformers
 ---
 
-# 🌟 CareerFlow AI — Token Classification Model
+# Model Card for Model ID
 
-CareerFlow AI is a powerful career-focused token classification and text-processing model built using **Hugging Face Transformers**, **PyTorch**, and **custom NLP pipelines**.
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model processes resumes, job descriptions, PDFs, CSVs, and unstructured career documents. It includes a hybrid system of OCR → parsing → clustering → DistilBERT-based classification.
 
----
 
-## 🚀 Key Features
+## Model Details
 
-- 🔍 **Token Classification** for career-related entities  
-- 📄 Supports resumes, job descriptions, and form data  
-- 🧠 DistilBERT backbone with custom fine-tuning  
-- 🔎 FAISS RAG retrieval system  
-- 🪄 Custom tokenizer + label encoder  
-- 📊 Complete training loop + evaluation script  
-- 🛠 Deployment-ready  
+### Model Description
 
----
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
 
-## 📦 Usage
+## Model Card Authors [optional]
 
-```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+[More Information Needed]
 
-model_name = "Sachin21112004/carrerflow-ai"
+## Model Card Contact
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForTokenClassification.from_pretrained(model_name)
+[More Information Needed]
+### Framework versions
 
-inputs = tokenizer("Senior software engineer with 4 years of experience...", return_tensors="pt")
-outputs = model(**inputs)
+- PEFT 0.18.0

adapter_config.json

@@ -29,8 +29,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "v_lin",
-    "q_lin"
+    "q_lin",
+    "v_lin"
   ],
   "target_parameters": null,
   "task_type": "FEATURE_EXTRACTION",

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f90b6786eb5ad0940780b0b65dcc673e67afb515c5d5fe791cb258bda1ee304a
+oid sha256:f7fef30fa7e4c41c333b83c9c9db6f0bc6e80943ccf973df33b9fc1f85f53235
 size 1183144

tokenizer.json

@@ -2,13 +2,13 @@
   "version": "1.0",
   "truncation": {
     "direction": "Right",
-    "max_length": 128,
+    "max_length": 256,
     "strategy": "LongestFirst",
     "stride": 0
   },
   "padding": {
     "strategy": {
-      "Fixed": 128
+      "Fixed": 256
     },
     "direction": "Right",
     "pad_to_multiple_of": null,