diff --git a/Named Entity Recognition using NLP/Dataset/label.json b/Named Entity Recognition using NLP/Dataset/label.json
new file mode 100644
index 000000000..ffa92759b
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/label.json	
@@ -0,0 +1 @@
+{"O": 0, "B-CARDINAL": 1, "B-DATE": 2, "I-DATE": 3, "B-PERSON": 4, "I-PERSON": 5, "B-NORP": 6, "B-GPE": 7, "I-GPE": 8, "B-LAW": 9, "I-LAW": 10, "B-ORG": 11, "I-ORG": 12, "B-PERCENT": 13, "I-PERCENT": 14, "B-ORDINAL": 15, "B-MONEY": 16, "I-MONEY": 17, "B-WORK_OF_ART": 18, "I-WORK_OF_ART": 19, "B-FAC": 20, "B-TIME": 21, "I-CARDINAL": 22, "B-LOC": 23, "B-QUANTITY": 24, "I-QUANTITY": 25, "I-NORP": 26, "I-LOC": 27, "B-PRODUCT": 28, "I-TIME": 29, "B-EVENT": 30, "I-EVENT": 31, "I-FAC": 32, "B-LANGUAGE": 33, "I-PRODUCT": 34, "I-ORDINAL": 35, "I-LANGUAGE": 36}
\ No newline at end of file
diff --git a/Named Entity Recognition using NLP/Dataset/test.json b/Named Entity Recognition using NLP/Dataset/test.json
new file mode 100644
index 000000000..aada4a047
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/test.json	
@@ -0,0 +1,8262 @@
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+{"tags": [11, 12, 12, 12], "tokens": ["Dow", "Chemical", "Co.", "--"]}
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+{"tags": [11, 12, 12, 12], "tokens": ["Centel", "Capital", "Corp.", "--"]}
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+{"tags": [0, 0, 0, 0, 0, 0, 0], "tokens": ["Pricing", "details", "were", "n't", "immediately", "available", "."]}
+{"tags": [11, 12, 12, 12, 12, 12], "tokens": ["Federal", "Home", "Loan", "Mortgage", "Corp.", "--"]}
+{"tags": [16, 17, 17, 0, 0, 0, 0, 0, 0, 11, 12, 12, 0], "tokens": ["$", "200", "million", "of", "stripped", "mortgage", "securities", "underwritten", "by", "BT", "Securities", "Corp", "."]}
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+{"tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "tokens": ["The", "collateral", "is", "being", "sold", "by", "a", "thrift", "institution", "."]}
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+{"tags": [0, 0, 0, 0, 0, 0, 11, 0], "tokens": ["Superconcentrates", "are", "n't", "entirely", "new", "for", "P&G", "."]}
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diff --git a/Named Entity Recognition using NLP/Dataset/train00.json b/Named Entity Recognition using NLP/Dataset/train00.json
new file mode 100644
index 000000000..4fef207bd
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/train00.json	
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diff --git a/Named Entity Recognition using NLP/Dataset/train01.json b/Named Entity Recognition using NLP/Dataset/train01.json
new file mode 100644
index 000000000..603cac0bd
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/train01.json	
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diff --git a/Named Entity Recognition using NLP/Dataset/train02.json b/Named Entity Recognition using NLP/Dataset/train02.json
new file mode 100644
index 000000000..541b9ba80
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/train02.json	
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diff --git a/Named Entity Recognition using NLP/Dataset/train03.json b/Named Entity Recognition using NLP/Dataset/train03.json
new file mode 100644
index 000000000..61903076c
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/train03.json	
@@ -0,0 +1,14924 @@
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diff --git a/Named Entity Recognition using NLP/Dataset/valid.json b/Named Entity Recognition using NLP/Dataset/valid.json
new file mode 100644
index 000000000..551a5a72b
--- /dev/null
+++ b/Named Entity Recognition using NLP/Dataset/valid.json	
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "224b2140",
+   "metadata": {},
+   "source": [
+    "# OntoNotes 5 NER EDA\n",
+    "EDA for the OntoNotes 5 NER dataset in the Dataset folder. Plots are saved in the Images folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6fa058c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['../Dataset/train00.json',\n",
+       " '../Dataset/train01.json',\n",
+       " '../Dataset/train02.json',\n",
+       " '../Dataset/train03.json',\n",
+       " '../Dataset/valid.json',\n",
+       " '../Dataset/test.json']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "from collections import Counter\n",
+    "from glob import glob\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "DATASET_DIR = os.path.join(\"..\", \"Dataset\")\n",
+    "IMAGES_DIR = os.path.join(\"..\", \"Images\")\n",
+    "os.makedirs(IMAGES_DIR, exist_ok=True)\n",
+    "\n",
+    "label_path = os.path.join(DATASET_DIR, \"label.json\")\n",
+    "with open(label_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "    label_to_id = json.load(f)\n",
+    "    id_to_label = {v: k for k, v in label_to_id.items()}\n",
+    "\n",
+    "data_files = sorted(glob(os.path.join(DATASET_DIR, \"train*.json\")))\n",
+    "data_files += [\n",
+    "    os.path.join(DATASET_DIR, \"valid.json\"),\n",
+    "    os.path.join(DATASET_DIR, \"test.json\"),\n",
+    "]\n",
+    "data_files = [p for p in data_files if os.path.exists(p)]\n",
+    "data_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "180affc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(76714, 1388883)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records = []\n",
+    "for path in data_files:\n",
+    "    with open(path, \"r\", encoding=\"utf-8\") as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip()\n",
+    "            if not line:\n",
+    "                continue\n",
+    "            item = json.loads(line)\n",
+    "            records.append(item)\n",
+    "\n",
+    "num_sentences = len(records)\n",
+    "num_tokens = sum(len(r[\"tokens\"]) for r in records)\n",
+    "num_sentences, num_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f32bf13d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('O', 1198624),\n",
+       " ('I-ORG', 22988),\n",
+       " ('B-GPE', 19913),\n",
+       " ('B-PERSON', 19437),\n",
+       " ('I-DATE', 17153)]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def decode_labels(tag_ids):\n",
+    "    return [id_to_label[t] for t in tag_ids]\n",
+    "\n",
+    "def iter_entities(tokens, tag_ids):\n",
+    "    labels = decode_labels(tag_ids)\n",
+    "    i = 0\n",
+    "    while i < len(labels):\n",
+    "        label = labels[i]\n",
+    "        if label.startswith(\"B-\"):\n",
+    "            ent_type = label[2:]\n",
+    "            start = i\n",
+    "            i += 1\n",
+    "            while i < len(labels) and labels[i] == f\"I-{ent_type}\":\n",
+    "                i += 1\n",
+    "            end = i\n",
+    "            text = \" \".join(tokens[start:end])\n",
+    "            yield ent_type, text, end - start\n",
+    "        else:\n",
+    "            i += 1\n",
+    "\n",
+    "sentence_lengths = [len(r[\"tokens\"]) for r in records]\n",
+    "tag_counter = Counter()\n",
+    "entity_type_counter = Counter()\n",
+    "entity_text_counter = Counter()\n",
+    "entity_span_lengths = []\n",
+    "\n",
+    "for r in records:\n",
+    "    tag_counter.update(decode_labels(r[\"tags\"]))\n",
+    "    for ent_type, text, span_len in iter_entities(r[\"tokens\"], r[\"tags\"]):\n",
+    "        entity_type_counter[ent_type] += 1\n",
+    "        entity_text_counter[text] += 1\n",
+    "        entity_span_lengths.append(span_len)\n",
+    "\n",
+    "tag_counter.most_common(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b2da6150",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: ../Images/label_distribution_top25.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: ../Images/sentence_length_distribution.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: ../Images/entity_span_length_distribution.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: ../Images/top_entities.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def save_plot(fig, filename):\n",
+    "    path = os.path.join(IMAGES_DIR, filename)\n",
+    "    fig.tight_layout()\n",
+    "    fig.savefig(path, dpi=150)\n",
+    "    print(f\"Saved: {path}\")\n",
+    "\n",
+    "# Label distribution (excluding O for readability)\n",
+    "label_counts = pd.Series(tag_counter).sort_values(ascending=False)\n",
+    "label_counts_no_o = label_counts.drop(labels=[\"O\"], errors=\"ignore\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "label_counts_no_o.head(25).sort_values().plot(kind=\"barh\", ax=ax)\n",
+    "ax.set_title(\"Top 25 NER Labels (excluding O)\")\n",
+    "ax.set_xlabel(\"Token Count\")\n",
+    "save_plot(fig, \"label_distribution_top25.png\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Sentence length distribution\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "ax.hist(sentence_lengths, bins=50, color=\"#4C72B0\")\n",
+    "ax.set_title(\"Sentence Length Distribution\")\n",
+    "ax.set_xlabel(\"Tokens per sentence\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "save_plot(fig, \"sentence_length_distribution.png\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Entity span length distribution\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "ax.hist(entity_span_lengths, bins=20, color=\"#55A868\")\n",
+    "ax.set_title(\"Entity Span Length Distribution\")\n",
+    "ax.set_xlabel(\"Span length (tokens)\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "save_plot(fig, \"entity_span_length_distribution.png\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Top entity texts\n",
+    "top_entities = pd.Series(entity_text_counter).sort_values(ascending=False).head(25)\n",
+    "fig, ax = plt.subplots(figsize=(10, 7))\n",
+    "top_entities.sort_values().plot(kind=\"barh\", ax=ax)\n",
+    "ax.set_title(\"Top 25 Entity Surface Forms\")\n",
+    "ax.set_xlabel(\"Count\")\n",
+    "save_plot(fig, \"top_entities.png\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfff45f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Named Entity Recognition using NLP",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Named Entity Recognition using NLP/Models/named_entity_recofnition_model.ipynb b/Named Entity Recognition using NLP/Models/named_entity_recofnition_model.ipynb
new file mode 100644
index 000000000..0c9f426df
--- /dev/null
+++ b/Named Entity Recognition using NLP/Models/named_entity_recofnition_model.ipynb	
@@ -0,0 +1,424 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11d9c70e",
+   "metadata": {},
+   "source": [
+    "# NER Model From Scratch\n",
+    "Train a simple BiLSTM tagger on OntoNotes 5 NER. Saves per-epoch weights in Streamlit and metrics plots in Images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "50ab6cfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "from collections import Counter\n",
+    "from glob import glob\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.utils.data import DataLoader, Dataset\n",
+    "\n",
+    "DATASET_DIR = os.path.join(\"..\", \"Dataset\")\n",
+    "IMAGES_DIR = os.path.join(\"..\", \"Images\")\n",
+    "STREAMLIT_DIR = os.path.join(\"..\", \"Streamlit\")\n",
+    "os.makedirs(IMAGES_DIR, exist_ok=True)\n",
+    "os.makedirs(STREAMLIT_DIR, exist_ok=True)\n",
+    "\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "SEED = 42\n",
+    "torch.manual_seed(SEED)\n",
+    "np.random.seed(SEED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19f32990",
+   "metadata": {},
+   "source": [
+    "## Data and paths\n",
+    "Set paths, output folders, and random seeds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a62111fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(59924, 8528, 8262)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_path = os.path.join(DATASET_DIR, \"label.json\")\n",
+    "with open(label_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "    label_to_id = json.load(f)\n",
+    "    id_to_label = {v: k for k, v in label_to_id.items()}\n",
+    "\n",
+    "train_files = sorted(glob(os.path.join(DATASET_DIR, \"train*.json\")))\n",
+    "valid_file = os.path.join(DATASET_DIR, \"valid.json\")\n",
+    "test_file = os.path.join(DATASET_DIR, \"test.json\")\n",
+    "\n",
+    "def load_records(paths):\n",
+    "    records = []\n",
+    "    for path in paths:\n",
+    "        if not os.path.exists(path):\n",
+    "            continue\n",
+    "        with open(path, \"r\", encoding=\"utf-8\") as f:\n",
+    "            for line in f:\n",
+    "                line = line.strip()\n",
+    "                if not line:\n",
+    "                    continue\n",
+    "                records.append(json.loads(line))\n",
+    "    return records\n",
+    "\n",
+    "train_records = load_records(train_files)\n",
+    "valid_records = load_records([valid_file])\n",
+    "test_records = load_records([test_file])\n",
+    "\n",
+    "len(train_records), len(valid_records), len(test_records)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf378665",
+   "metadata": {},
+   "source": [
+    "## Load datasets\n",
+    "Read OntoNotes 5 JSONL files and split into train, validation, and test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9bb5458f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(41326, 37)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PAD_TOKEN = \"<PAD>\"\n",
+    "UNK_TOKEN = \"<UNK>\"\n",
+    "\n",
+    "token_counter = Counter()\n",
+    "for r in train_records:\n",
+    "    token_counter.update(r[\"tokens\"])\n",
+    "\n",
+    "vocab = [PAD_TOKEN, UNK_TOKEN] + [t for t, _ in token_counter.most_common()]\n",
+    "token_to_id = {t: i for i, t in enumerate(vocab)}\n",
+    "\n",
+    "pad_id = token_to_id[PAD_TOKEN]\n",
+    "unk_id = token_to_id[UNK_TOKEN]\n",
+    "num_labels = len(label_to_id)\n",
+    "\n",
+    "def encode_record(record):\n",
+    "    tokens = record[\"tokens\"]\n",
+    "    tags = record[\"tags\"]\n",
+    "    token_ids = [token_to_id.get(t, unk_id) for t in tokens]\n",
+    "    tag_ids = tags\n",
+    "    return token_ids, tag_ids\n",
+    "\n",
+    "def pad_batch(batch):\n",
+    "    max_len = max(len(x[0]) for x in batch)\n",
+    "    token_batch = []\n",
+    "    tag_batch = []\n",
+    "    mask_batch = []\n",
+    "    for token_ids, tag_ids in batch:\n",
+    "        pad_len = max_len - len(token_ids)\n",
+    "        token_batch.append(token_ids + [pad_id] * pad_len)\n",
+    "        tag_batch.append(tag_ids + [-100] * pad_len)\n",
+    "        mask_batch.append([1] * len(token_ids) + [0] * pad_len)\n",
+    "    return (\n",
+    "        torch.tensor(token_batch, dtype=torch.long),\n",
+    "        torch.tensor(tag_batch, dtype=torch.long),\n",
+    "        torch.tensor(mask_batch, dtype=torch.float32),\n",
+    "    )\n",
+    "\n",
+    "class NERDataset(Dataset):\n",
+    "    def __init__(self, records):\n",
+    "        self.records = records\n",
+    "    def __len__(self):\n",
+    "        return len(self.records)\n",
+    "    def __getitem__(self, idx):\n",
+    "        return encode_record(self.records[idx])\n",
+    "\n",
+    "train_ds = NERDataset(train_records)\n",
+    "valid_ds = NERDataset(valid_records)\n",
+    "test_ds = NERDataset(test_records)\n",
+    "\n",
+    "train_loader = DataLoader(train_ds, batch_size=32, shuffle=True, collate_fn=pad_batch)\n",
+    "valid_loader = DataLoader(valid_ds, batch_size=64, shuffle=False, collate_fn=pad_batch)\n",
+    "test_loader = DataLoader(test_ds, batch_size=64, shuffle=False, collate_fn=pad_batch)\n",
+    "\n",
+    "len(vocab), num_labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba84cd3e",
+   "metadata": {},
+   "source": [
+    "## Vocabulary and batching\n",
+    "Build token vocabulary, encode samples, and create padded batches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "153db931",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BiLSTMTagger(nn.Module):\n",
+    "    def __init__(self, vocab_size, embed_dim, hidden_dim, num_labels, pad_id):\n",
+    "        super().__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_id)\n",
+    "        self.lstm = nn.LSTM(\n",
+    "            embed_dim,\n",
+    "            hidden_dim,\n",
+    "            num_layers=1,\n",
+    "            batch_first=True,\n",
+    "            bidirectional=True,\n",
+    "        )\n",
+    "        self.dropout = nn.Dropout(0.2)\n",
+    "        self.classifier = nn.Linear(hidden_dim * 2, num_labels)\n",
+    "\n",
+    "    def forward(self, input_ids, mask):\n",
+    "        x = self.embedding(input_ids)\n",
+    "        x, _ = self.lstm(x)\n",
+    "        x = self.dropout(x)\n",
+    "        logits = self.classifier(x)\n",
+    "        return logits\n",
+    "\n",
+    "model = BiLSTMTagger(\n",
+    "    vocab_size=len(vocab),\n",
+    "    embed_dim=128,\n",
+    "    hidden_dim=256,\n",
+    "    num_labels=num_labels,\n",
+    "    pad_id=pad_id,\n",
+    ").to(DEVICE)\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss(ignore_index=-100)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c8b302f",
+   "metadata": {},
+   "source": [
+    "## Model\n",
+    "Define a BiLSTM tagger and optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39f93ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 01 | train loss 0.3595 acc 0.9184 | val loss 0.2092 acc 0.9425\n",
+      "Epoch 02 | train loss 0.1501 acc 0.9602 | val loss 0.1669 acc 0.9542\n",
+      "Epoch 03 | train loss 0.0962 acc 0.9733 | val loss 0.1614 acc 0.9557\n",
+      "Epoch 04 | train loss 0.0646 acc 0.9814 | val loss 0.1596 acc 0.9575\n",
+      "Epoch 05 | train loss 0.0431 acc 0.9874 | val loss 0.1725 acc 0.9566\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_epoch(model, loader, train=False):\n",
+    "    total_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_tokens = 0\n",
+    "    model.train(train)\n",
+    "    for input_ids, tag_ids, mask in loader:\n",
+    "        input_ids = input_ids.to(DEVICE)\n",
+    "        tag_ids = tag_ids.to(DEVICE)\n",
+    "        mask = mask.to(DEVICE)\n",
+    "\n",
+    "        logits = model(input_ids, mask)\n",
+    "        loss = criterion(logits.view(-1, num_labels), tag_ids.view(-1))\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        total_loss += loss.item()\n",
+    "        with torch.no_grad():\n",
+    "            preds = logits.argmax(-1)\n",
+    "            valid = tag_ids != -100\n",
+    "            total_correct += (preds[valid] == tag_ids[valid]).sum().item()\n",
+    "            total_tokens += valid.sum().item()\n",
+    "    avg_loss = total_loss / max(len(loader), 1)\n",
+    "    accuracy = total_correct / max(total_tokens, 1)\n",
+    "    return avg_loss, accuracy\n",
+    "\n",
+    "num_epochs = 5\n",
+    "history = {\"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\n",
+    "\n",
+    "for epoch in range(1, num_epochs + 1):\n",
+    "    train_loss, train_acc = run_epoch(model, train_loader, train=True)\n",
+    "    val_loss, val_acc = run_epoch(model, valid_loader, train=False)\n",
+    "    history[\"train_loss\"].append(train_loss)\n",
+    "    history[\"train_acc\"].append(train_acc)\n",
+    "    history[\"val_loss\"].append(val_loss)\n",
+    "    history[\"val_acc\"].append(val_acc)\n",
+    "\n",
+    "    print(f\"Epoch {epoch:02d} | train loss {train_loss:.4f} acc {train_acc:.4f} | val loss {val_loss:.4f} acc {val_acc:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "24b963b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weight_path = os.path.join(STREAMLIT_DIR, f\"ner_model.pt\")\n",
+    "torch.save(model.state_dict(), weight_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c3c3150",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "Run epochs, track loss and accuracy, and save weights per epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6f912784",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test loss 0.1632 | Test acc 0.9602\n",
+      "Saved: ../Images/ner_training_metrics.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved: ../Images/ner_final_metrics.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x250 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_loss, test_acc = run_epoch(model, test_loader, train=False)\n",
+    "print(f\"Test loss {test_loss:.4f} | Test acc {test_acc:.4f}\")\n",
+    "\n",
+    "epochs = np.arange(1, num_epochs + 1)\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "ax[0].plot(epochs, history[\"train_loss\"], label=\"train\")\n",
+    "ax[0].plot(epochs, history[\"val_loss\"], label=\"val\")\n",
+    "ax[0].set_title(\"Loss\")\n",
+    "ax[0].set_xlabel(\"Epoch\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(epochs, history[\"train_acc\"], label=\"train\")\n",
+    "ax[1].plot(epochs, history[\"val_acc\"], label=\"val\")\n",
+    "ax[1].set_title(\"Accuracy\")\n",
+    "ax[1].set_xlabel(\"Epoch\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "metrics_path = os.path.join(IMAGES_DIR, \"ner_training_metrics.png\")\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(metrics_path, dpi=150)\n",
+    "print(f\"Saved: {metrics_path}\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Final metrics summary image\n",
+    "fig, ax = plt.subplots(figsize=(6, 2.5))\n",
+    "ax.axis(\"off\")\n",
+    "table = ax.table(\n",
+    "    cellText=[[f\"{test_loss:.4f}\", f\"{test_acc:.4f}\"]],\n",
+    "    colLabels=[\"Test Loss\", \"Test Accuracy\"],\n",
+    "    loc=\"center\",\n",
+    ")\n",
+    "table.scale(1, 1.5)\n",
+    "summary_path = os.path.join(IMAGES_DIR, \"ner_final_metrics.png\")\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(summary_path, dpi=150)\n",
+    "print(f\"Saved: {summary_path}\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Named Entity Recognition using NLP",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Named Entity Recognition using NLP/README.md b/Named Entity Recognition using NLP/README.md
new file mode 100644
index 000000000..e9c3a7f48
--- /dev/null
+++ b/Named Entity Recognition using NLP/README.md	
@@ -0,0 +1,63 @@
+## **PROJECT TITLE**
+
+Named Entity Recognition Using NLP (OntoNotes 5)
+
+### 🎯 **Goal**
+
+Build a named entity recognition system from scratch, explore the dataset, and provide an interactive Streamlit demo.
+
+### 🧵 **Dataset**
+
+OntoNotes 5.0 (LDC2013T19). Obtain via the Linguistic Data Consortium (LDC).
+
+### 🧾 **Description**
+
+This project trains a BiLSTM tagger for NER using the OntoNotes 5 JSONL dataset. It includes EDA plots, model training with per-epoch checkpoints, and a Streamlit app for inference.
+
+### 🧮 **What I had done!**
+
+- Loaded OntoNotes 5 JSONL splits and label mapping.
+- Ran EDA to understand label distribution, sentence length, entity span length, and top entity forms.
+- Built a token vocabulary from the training split.
+- Trained a BiLSTM tagger with padded batches.
+- Saved per-epoch model weights to the Streamlit folder.
+- Generated training metrics and final metrics images.
+- Built a Streamlit app for quick inference.
+
+### 🚀 **Models Implemented**
+
+- BiLSTM tagger (from scratch) for sequence labeling: simple, fast, and effective baseline for NER without relying on pretrained transformers.
+
+### 📚 **Libraries Needed**
+
+- torch
+- numpy
+- pandas
+- matplotlib
+- streamlit
+
+### 📊 **Exploratory Data Analysis Results**
+
+`INCLUSION OF IMAGES OF THE VISUALIZATION IS MUST (RESULT OF EDA).`
+
+![Label Distribution](Images/label_distribution_top25.png)
+
+![Sentence Length Distribution](Images/sentence_length_distribution.png)
+
+![Entity Span Length Distribution](Images/entity_span_length_distribution.png)
+
+![Top Entity Surface Forms](Images/top_entities.png)
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+- BiLSTM tagger: ![Results](Images/ner_final_metrics.png)
+
+
+### 📢 **Conclusion**
+
+The BiLSTM baseline provides a solid starting point for OntoNotes 5 NER. Use the final metrics image to report test accuracy after training.
+
+
+Gaurav Upreti
+[GitHub](https://github.com/Reaper-ai) | [LinkedIn](https://www.linkedin.com/in/gaurav-upreti-488348312/)
+Contribution under GSSoC 2026
\ No newline at end of file
diff --git a/Named Entity Recognition using NLP/Streamlit/app.py b/Named Entity Recognition using NLP/Streamlit/app.py
new file mode 100644
index 000000000..98aba3624
--- /dev/null
+++ b/Named Entity Recognition using NLP/Streamlit/app.py	
@@ -0,0 +1,137 @@
+import json
+import os
+from glob import glob
+
+import streamlit as st
+import torch
+from torch import nn
+
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+DATASET_DIR = os.path.join(BASE_DIR, "Dataset")
+MODEL_PATH = os.path.join(os.path.dirname(__file__), "ner_model.pt")
+
+st.set_page_config(page_title="NER Demo", layout="centered")
+
+st.title("Named Entity Recognition Demo")
+st.caption("BiLSTM tagger trained on OntoNotes 5 (simple whitespace tokenization).")
+
+HUMAN_LABELS = {
+    "PERSON": "Person",
+    "ORG": "Organization",
+    "GPE": "Geo-Political Entity",
+    "NORP": "Nationality/Religious/Political Group",
+    "FAC": "Facility",
+    "LOC": "Location",
+    "PRODUCT": "Product",
+    "EVENT": "Event",
+    "WORK_OF_ART": "Work of Art",
+    "LAW": "Law",
+    "LANGUAGE": "Language",
+    "DATE": "Date",
+    "TIME": "Time",
+    "MONEY": "Money",
+    "PERCENT": "Percent",
+    "QUANTITY": "Quantity",
+    "ORDINAL": "Ordinal",
+    "CARDINAL": "Cardinal",
+}
+
+def format_label(label):
+    if label == "O":
+        return "O"
+    if "-" not in label:
+        return label
+    bio, ent_type = label.split("-", 1)
+    human = HUMAN_LABELS.get(ent_type, ent_type)
+    return f"{human} ({bio})"
+
+@st.cache_resource
+def load_label_map():
+    label_path = os.path.join(DATASET_DIR, "label.json")
+    with open(label_path, "r", encoding="utf-8") as f:
+        label_to_id = json.load(f)
+    id_to_label = {v: k for k, v in label_to_id.items()}
+    return id_to_label
+
+@st.cache_resource
+def build_vocab():
+    token_counter = {}
+    train_files = sorted(glob(os.path.join(DATASET_DIR, "train*.json")))
+    for path in train_files:
+        if not os.path.exists(path):
+            continue
+        with open(path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                item = json.loads(line)
+                for token in item["tokens"]:
+                    token_counter[token] = token_counter.get(token, 0) + 1
+
+    pad_token = "<PAD>"
+    unk_token = "<UNK>"
+    vocab = [pad_token, unk_token] + sorted(
+        token_counter.keys(), key=lambda t: token_counter[t], reverse=True
+    )
+    token_to_id = {t: i for i, t in enumerate(vocab)}
+    return token_to_id
+
+class BiLSTMTagger(nn.Module):
+    def __init__(self, vocab_size, embed_dim, hidden_dim, num_labels, pad_id):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_id)
+        self.lstm = nn.LSTM(
+            embed_dim,
+            hidden_dim,
+            num_layers=1,
+            batch_first=True,
+            bidirectional=True,
+        )
+        self.dropout = nn.Dropout(0.2)
+        self.classifier = nn.Linear(hidden_dim * 2, num_labels)
+
+    def forward(self, input_ids):
+        x = self.embedding(input_ids)
+        x, _ = self.lstm(x)
+        x = self.dropout(x)
+        logits = self.classifier(x)
+        return logits
+
+@st.cache_resource
+def load_model(token_to_id, id_to_label):
+    pad_id = token_to_id["<PAD>"]
+    model = BiLSTMTagger(
+        vocab_size=len(token_to_id),
+        embed_dim=128,
+        hidden_dim=256,
+        num_labels=len(id_to_label),
+        pad_id=pad_id,
+    )
+    model.load_state_dict(torch.load(MODEL_PATH, map_location="cpu"))
+    model.eval()
+    return model
+
+id_to_label = load_label_map()
+token_to_id = build_vocab()
+model = load_model(token_to_id, id_to_label)
+
+text = st.text_area("Enter text", "Barack Obama visited New York in 2012.")
+
+if st.button("Tag Entities"):
+    tokens = text.strip().split()
+    if not tokens:
+        st.warning("Please enter some text.")
+    else:
+        unk_id = token_to_id.get("<UNK>")
+        input_ids = torch.tensor([[token_to_id.get(t, unk_id) for t in tokens]])
+        with torch.no_grad():
+            logits = model(input_ids)
+            pred_ids = logits.argmax(-1).squeeze(0).tolist()
+        pred_labels = [format_label(id_to_label.get(i, "O")) for i in pred_ids]
+
+        st.subheader("Predictions")
+        st.dataframe({"token": tokens, "label": pred_labels}, use_container_width=True)
+
+st.sidebar.markdown("**Model weights**")
+st.sidebar.code(MODEL_PATH)
diff --git a/Named Entity Recognition using NLP/Streamlit/ner_model.pt b/Named Entity Recognition using NLP/Streamlit/ner_model.pt
new file mode 100644
index 000000000..998c9a745
Binary files /dev/null and b/Named Entity Recognition using NLP/Streamlit/ner_model.pt differ
diff --git a/Named Entity Recognition using NLP/pyproject.toml b/Named Entity Recognition using NLP/pyproject.toml
new file mode 100644
index 000000000..f522b7849
--- /dev/null
+++ b/Named Entity Recognition using NLP/pyproject.toml	
@@ -0,0 +1,14 @@
+[project]
+name = "named-entity-recognition-using-nlp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "ipykernel>=7.2.0",
+    "matplotlib>=3.10.9",
+    "pandas>=3.0.3",
+    "scikit-learn>=1.8.0",
+    "streamlit>=1.58.0",
+    "torch>=2.12.0",
+]