Upload Resume NER model

README.md

@@ -0,0 +1,175 @@
+---
+datasets:
+- dataturks/resume-entities-for-ner
+language:
+- en
+license: apache-2.0
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: resume-ner-distilbert
+  results:
+  - dataset:
+      name: Resume NER
+      type: dataturks/resume-entities-for-ner
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.211
+    - name: Precision
+      type: precision
+      value: 0.2189
+    - name: Recall
+      type: recall
+      value: 0.2037
+    task:
+      name: Named Entity Recognition
+      type: token-classification
+pipeline_tag: token-classification
+tags:
+- ner
+- token-classification
+- resume
+- nlp
+- transformers
+- lora
+---
+
+# Resume Ner Distilbert
+
+## Model Description
+
+This is a fine-tuned Named Entity Recognition (NER) model for extracting structured information from resumes.
+The model is trained using **Fine-tuning with LoRA** on a distilbert-base-uncased backbone.
+
+### Supported Entity Types
+
+| Entity Type | Description | Example |
+|-------------|-------------|---------|
+| NAME | Person's name | "John Doe" |
+| EMAIL | Email address | "john@example.com" |
+| PHONE | Phone number | "+1 555-123-4567" |
+| LOCATION | Geographic location | "San Francisco, CA" |
+| ORG | Organization/Company | "Google Inc." |
+| TITLE | Job title | "Senior Software Engineer" |
+| DEGREE | Academic degree | "Bachelor of Science in Computer Science" |
+| SKILL | Technical or soft skill | "Python", "Machine Learning" |
+| CERT | Certification | "AWS Solutions Architect" |
+| DATE | Date or time period | "2020-2023", "January 2022" |
+
+## Training Details
+
+### Training Data
+- **Dataset**: Dataturks Resume Entities for NER
+- **Source**: [Kaggle](https://www.kaggle.com/datasets/dataturks/resume-entities-for-ner)
+- **Training Examples**: N/A
+- **Validation Examples**: N/A
+
+### Training Configuration
+- **Base Model**: distilbert-base-uncased
+- **Training Method**: Fine-tuning with LoRA
+- **Epochs**: 10
+- **Learning Rate**: 3e-05
+- **Batch Size**: 8
+- **Max Sequence Length**: 512
+- **Random Seed**: 42
+
+### Performance Metrics
+
+| Metric | Validation Set |
+|--------|----------------|
+| Precision | 0.2189 |
+| Recall | 0.2037 |
+| F1-Score | 0.2110 |
+
+## Usage
+
+### Using Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+# Load the model
+ner = pipeline("ner", model="Joshuant/resume-ner-distilbert", aggregation_strategy="simple")
+
+# Extract entities from resume text
+text = """
+John Doe
+Email: john.doe@email.com
+Phone: +1 555-123-4567
+
+EDUCATION
+Bachelor of Science in Computer Science, MIT, 2020
+
+EXPERIENCE
+Senior Software Engineer at Google, 2020-2023
+- Developed ML pipelines using Python and TensorFlow
+"""
+
+entities = ner(text)
+for entity in entities:
+    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")
+```
+
+### Using AutoModel
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Joshuant/resume-ner-distilbert")
+model = AutoModelForTokenClassification.from_pretrained("Joshuant/resume-ner-distilbert")
+
+# Tokenize input
+text = "John Doe, Senior Software Engineer at Google"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+
+# Decode predictions
+tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+labels = [model.config.id2label[p.item()] for p in predictions[0]]
+
+for token, label in zip(tokens, labels):
+    if label != "O":
+        print(f"{token}: {label}")
+```
+
+## Limitations
+
+- The model is primarily trained on English resumes and may not perform well on other languages
+- Performance may vary based on resume formatting and structure
+- The model may struggle with unusual entity formats or domain-specific terminology
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{resume_ner_slm_2026,
+    title={Context-Aware Resume NER with Small Language Models},
+    author={Research Team},
+    year={2026},
+    howpublished={\url{https://huggingface.co/Joshuant/resume-ner-distilbert}}
+}
+```
+
+## License
+
+This model is released under the apache-2.0 license.
+
+## Acknowledgments
+
+- Dataturks for the original Resume NER dataset
+- Hugging Face for the transformers library and model hosting
+- The open-source NLP community
+
+---
+
+*Model trained on 2026-01-08*

adapter_config.json

@@ -0,0 +1,44 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "distilbert-base-uncased",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "classifier",
+    "score"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_lin",
+    "v_lin"
+  ],
+  "target_parameters": null,
+  "task_type": "TOKEN_CLS",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a35dce7963415c693a4f9be32a5687834eef4d3c90c3ac013a77d8c7012c525
+size 664228

config.json

@@ -0,0 +1,73 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-Name",
+    "2": "I-Name",
+    "3": "B-Email Address",
+    "4": "I-Email Address",
+    "5": "B-Location",
+    "6": "I-Location",
+    "7": "B-Designation",
+    "8": "I-Designation",
+    "9": "B-Companies worked at",
+    "10": "I-Companies worked at",
+    "11": "B-College Name",
+    "12": "I-College Name",
+    "13": "B-Degree",
+    "14": "I-Degree",
+    "15": "B-Graduation Year",
+    "16": "I-Graduation Year",
+    "17": "B-Skills",
+    "18": "I-Skills",
+    "19": "B-Years of Experience",
+    "20": "I-Years of Experience",
+    "21": "B-UNKNOWN",
+    "22": "I-UNKNOWN"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-College Name": 11,
+    "B-Companies worked at": 9,
+    "B-Degree": 13,
+    "B-Designation": 7,
+    "B-Email Address": 3,
+    "B-Graduation Year": 15,
+    "B-Location": 5,
+    "B-Name": 1,
+    "B-Skills": 17,
+    "B-UNKNOWN": 21,
+    "B-Years of Experience": 19,
+    "I-College Name": 12,
+    "I-Companies worked at": 10,
+    "I-Degree": 14,
+    "I-Designation": 8,
+    "I-Email Address": 4,
+    "I-Graduation Year": 16,
+    "I-Location": 6,
+    "I-Name": 2,
+    "I-Skills": 18,
+    "I-UNKNOWN": 22,
+    "I-Years of Experience": 20,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.56.1",
+  "vocab_size": 30522
+}

eval_results.json

@@ -0,0 +1,10 @@
+{
+  "eval_loss": 0.4952681064605713,
+  "eval_precision": 0.21890547263681592,
+  "eval_recall": 0.2037037037037037,
+  "eval_f1": 0.21103117505995203,
+  "eval_runtime": 2.187,
+  "eval_samples_per_second": 7.316,
+  "eval_steps_per_second": 0.915,
+  "epoch": 10.0
+}

label_config.json

@@ -0,0 +1,65 @@
+{
+  "label2id": {
+    "O": 0,
+    "B-Name": 1,
+    "I-Name": 2,
+    "B-Email Address": 3,
+    "I-Email Address": 4,
+    "B-Location": 5,
+    "I-Location": 6,
+    "B-Designation": 7,
+    "I-Designation": 8,
+    "B-Companies worked at": 9,
+    "I-Companies worked at": 10,
+    "B-College Name": 11,
+    "I-College Name": 12,
+    "B-Degree": 13,
+    "I-Degree": 14,
+    "B-Graduation Year": 15,
+    "I-Graduation Year": 16,
+    "B-Skills": 17,
+    "I-Skills": 18,
+    "B-Years of Experience": 19,
+    "I-Years of Experience": 20,
+    "B-UNKNOWN": 21,
+    "I-UNKNOWN": 22
+  },
+  "id2label": {
+    "0": "O",
+    "1": "B-Name",
+    "2": "I-Name",
+    "3": "B-Email Address",
+    "4": "I-Email Address",
+    "5": "B-Location",
+    "6": "I-Location",
+    "7": "B-Designation",
+    "8": "I-Designation",
+    "9": "B-Companies worked at",
+    "10": "I-Companies worked at",
+    "11": "B-College Name",
+    "12": "I-College Name",
+    "13": "B-Degree",
+    "14": "I-Degree",
+    "15": "B-Graduation Year",
+    "16": "I-Graduation Year",
+    "17": "B-Skills",
+    "18": "I-Skills",
+    "19": "B-Years of Experience",
+    "20": "I-Years of Experience",
+    "21": "B-UNKNOWN",
+    "22": "I-UNKNOWN"
+  },
+  "label_list": [
+    "Name",
+    "Email Address",
+    "Location",
+    "Designation",
+    "Companies worked at",
+    "College Name",
+    "Degree",
+    "Graduation Year",
+    "Skills",
+    "Years of Experience",
+    "UNKNOWN"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d92e4503993ab3a990aa2884d60223003f74d1ee2c4079883836e1dc6dbc954
+size 265534612

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:802547bfa77871f608619326ae1119181b27b35ef1d47676aeca086f6b0b8dfa
+size 5777

training_config.yaml

@@ -0,0 +1,51 @@
+data:
+  label_list:
+  - Name
+  - Email Address
+  - Location
+  - Designation
+  - Companies worked at
+  - College Name
+  - Degree
+  - Graduation Year
+  - Skills
+  - Years of Experience
+  - UNKNOWN
+  max_length: 512
+  stride: 96
+  train_jsonl: data/processed/train.jsonl
+  use_context_tokens: false
+  valid_jsonl: data/processed/valid.jsonl
+model:
+  base_model: distilbert-base-uncased
+  load_in_4bit: false
+  lora:
+    alpha: 16
+    dropout: 0.1
+    enabled: false
+    r: 8
+    target_modules:
+    - q_lin
+    - v_lin
+  task: token_classification
+  torch_dtype: float32
+run_name: distilbert_resume_ner
+seed: 42
+tracking:
+  mlflow_experiment: resume_ner
+  use_mlflow: false
+train:
+  batch_size: 8
+  bf16: false
+  early_stopping_patience: 5
+  epochs: 10
+  eval_steps: 20
+  fp16: false
+  grad_accum: 1
+  logging_steps: 10
+  lr: 3.0e-05
+  metric_for_best_model: eval_f1
+  output_dir: outputs/${run_name}
+  save_steps: 20
+  warmup_ratio: 0.1
+  weight_decay: 0.01