diff --git "a/stella_en_400m_obliqa.ipynb" "b/stella_en_400m_obliqa.ipynb"
new file mode 100644--- /dev/null
+++ "b/stella_en_400m_obliqa.ipynb"
@@ -0,0 +1,10031 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "byfhCh_mJB_K",
+        "outputId": "fe485abb-ca7c-45a0-c493-e8c7cde3b9f1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/huggingface/accelerate\n",
+            "  Cloning https://github.com/huggingface/accelerate to /tmp/pip-req-build-jjzy91h2\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate /tmp/pip-req-build-jjzy91h2\n",
+            "  Resolved https://github.com/huggingface/accelerate to commit e11d3ceff3a49378796cdff5b466586d877d5c60\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (1.26.4)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (24.2)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (5.9.5)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (6.0.2)\n",
+            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (2.5.1+cu121)\n",
+            "Requirement already satisfied: huggingface_hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (0.26.2)\n",
+            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (0.4.5)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.16.1)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2024.10.0)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (4.66.6)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (3.1.4)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate==1.2.0.dev0) (1.3.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==1.2.0.dev0) (3.0.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2024.8.30)\n",
+            "Building wheels for collected packages: accelerate\n",
+            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for accelerate: filename=accelerate-1.2.0.dev0-py3-none-any.whl size=334925 sha256=fe7e7fe23693551890b431567674db7621ed125f02fa185baf8c2737226c7307\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ex33hk33/wheels/f6/c7/9d/1b8a5ca8353d9307733bc719107acb67acdc95063bba749f26\n",
+            "Successfully built accelerate\n",
+            "Installing collected packages: accelerate\n",
+            "  Attempting uninstall: accelerate\n",
+            "    Found existing installation: accelerate 1.1.1\n",
+            "    Uninstalling accelerate-1.1.1:\n",
+            "      Successfully uninstalled accelerate-1.1.1\n",
+            "Successfully installed accelerate-1.2.0.dev0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/huggingface/accelerate\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -qqqU xformers"
+      ],
+      "metadata": {
+        "id": "BFDAPw3lLh-J",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8054de24-df98-45a3-fbc5-77fa71bc5ddb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/16.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/16.7 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:03\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/16.7 MB\u001b[0m \u001b[31m53.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/16.7 MB\u001b[0m \u001b[31m96.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m211.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m211.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "# Disable all integrations like WandB explicitly using the --report_to flag\n",
+        "os.environ[\"TRANSFORMERS_NO_LOGGING\"] = \"true\"\n",
+        "os.environ[\"WANDB_MODE\"] = \"disabled\""
+      ],
+      "metadata": {
+        "id": "mY-Nkz3qM4AB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ANFexbEi5IWE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+        "outputId": "222c8a16-9936-43af-c659-bb2c6f6daabd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m471.0/480.6 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m \u001b[31m91.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.3/245.3 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+            "  from tqdm.autonotebook import tqdm, trange\n",
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "006a724e98cd493ca0ce77169a472148"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config_sentence_transformers.json:   0%|          | 0.00/397 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9f9dd00eed0346e4b0cd58365ff7bc79"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/170k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "65711503ae1545e0aa5f949f4babc06f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/51.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0b950b7e32de47eca6d271c7a2aaa391"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/892 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3d5973fc5633454c808d849f3178edb9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "configuration.py:   0%|          | 0.00/7.13k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a9e769e12bfd4fdc9eb9611acc2ebe3c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "A new version of the following files was downloaded from https://huggingface.co/dunzhang/stella_en_400M_v5:\n",
+            "- configuration.py\n",
+            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "modeling.py:   0%|          | 0.00/57.5k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f74d27c20cd64e8f87d0b7faa178266b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "A new version of the following files was downloaded from https://huggingface.co/dunzhang/stella_en_400M_v5:\n",
+            "- modeling.py\n",
+            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/1.74G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b1d76d71ef3d410d9374ef71471a6661"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of the model checkpoint at dunzhang/stella_en_400M_v5 were not used when initializing NewModel: ['new.pooler.dense.bias', 'new.pooler.dense.weight']\n",
+            "- This IS expected if you are initializing NewModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing NewModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bae55224df5741f78fe3e17ef0de4e53"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+        {
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+            ],
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "1_Pooling/config.json:   0%|          | 0.00/186 [00:00<?, ?B/s]"
+            ],
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+            ],
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/4.20M [00:00<?, ?B/s]"
+            ],
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "2_Dense_1024/config.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "ObliQA_train.json:   0%|          | 0.00/28.9M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "ObliQA_dev.json:   0%|          | 0.00/3.62M [00:00<?, ?B/s]"
+            ],
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+              "version_major": 2,
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "ObliQA_test.json:   0%|          | 0.00/3.59M [00:00<?, ?B/s]"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/22295 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/2788 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/2786 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3cdeecb3fb0347a5ad749da82603746f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='601' max='11150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [  601/11150 16:07 < 4:44:01, 0.62 it/s, Epoch 0.27/5]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Cosine Accuracy@1</th>\n",
+              "      <th>Cosine Accuracy@3</th>\n",
+              "      <th>Cosine Accuracy@5</th>\n",
+              "      <th>Cosine Accuracy@10</th>\n",
+              "      <th>Cosine Precision@1</th>\n",
+              "      <th>Cosine Precision@3</th>\n",
+              "      <th>Cosine Precision@5</th>\n",
+              "      <th>Cosine Precision@10</th>\n",
+              "      <th>Cosine Recall@1</th>\n",
+              "      <th>Cosine Recall@3</th>\n",
+              "      <th>Cosine Recall@5</th>\n",
+              "      <th>Cosine Recall@10</th>\n",
+              "      <th>Cosine Ndcg@10</th>\n",
+              "      <th>Cosine Mrr@10</th>\n",
+              "      <th>Cosine Map@100</th>\n",
+              "      <th>Dot Accuracy@1</th>\n",
+              "      <th>Dot Accuracy@3</th>\n",
+              "      <th>Dot Accuracy@5</th>\n",
+              "      <th>Dot Accuracy@10</th>\n",
+              "      <th>Dot Precision@1</th>\n",
+              "      <th>Dot Precision@3</th>\n",
+              "      <th>Dot Precision@5</th>\n",
+              "      <th>Dot Precision@10</th>\n",
+              "      <th>Dot Recall@1</th>\n",
+              "      <th>Dot Recall@3</th>\n",
+              "      <th>Dot Recall@5</th>\n",
+              "      <th>Dot Recall@10</th>\n",
+              "      <th>Dot Ndcg@10</th>\n",
+              "      <th>Dot Mrr@10</th>\n",
+              "      <th>Dot Map@100</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>200</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>0.639885</td>\n",
+              "      <td>0.755739</td>\n",
+              "      <td>0.781923</td>\n",
+              "      <td>0.806671</td>\n",
+              "      <td>0.639885</td>\n",
+              "      <td>0.265184</td>\n",
+              "      <td>0.167575</td>\n",
+              "      <td>0.088773</td>\n",
+              "      <td>0.562865</td>\n",
+              "      <td>0.674617</td>\n",
+              "      <td>0.703288</td>\n",
+              "      <td>0.732927</td>\n",
+              "      <td>0.674471</td>\n",
+              "      <td>0.700678</td>\n",
+              "      <td>0.639416</td>\n",
+              "      <td>0.596126</td>\n",
+              "      <td>0.725251</td>\n",
+              "      <td>0.760043</td>\n",
+              "      <td>0.796270</td>\n",
+              "      <td>0.596126</td>\n",
+              "      <td>0.253348</td>\n",
+              "      <td>0.162482</td>\n",
+              "      <td>0.087339</td>\n",
+              "      <td>0.528222</td>\n",
+              "      <td>0.649462</td>\n",
+              "      <td>0.684015</td>\n",
+              "      <td>0.722388</td>\n",
+              "      <td>0.649568</td>\n",
+              "      <td>0.666984</td>\n",
+              "      <td>0.611385</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>400</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>0.653156</td>\n",
+              "      <td>0.757891</td>\n",
+              "      <td>0.785509</td>\n",
+              "      <td>0.809182</td>\n",
+              "      <td>0.653156</td>\n",
+              "      <td>0.265423</td>\n",
+              "      <td>0.168436</td>\n",
+              "      <td>0.089383</td>\n",
+              "      <td>0.573284</td>\n",
+              "      <td>0.675508</td>\n",
+              "      <td>0.705171</td>\n",
+              "      <td>0.735808</td>\n",
+              "      <td>0.681055</td>\n",
+              "      <td>0.709550</td>\n",
+              "      <td>0.647246</td>\n",
+              "      <td>0.617647</td>\n",
+              "      <td>0.736729</td>\n",
+              "      <td>0.771879</td>\n",
+              "      <td>0.804878</td>\n",
+              "      <td>0.617647</td>\n",
+              "      <td>0.258489</td>\n",
+              "      <td>0.165495</td>\n",
+              "      <td>0.088522</td>\n",
+              "      <td>0.543974</td>\n",
+              "      <td>0.658268</td>\n",
+              "      <td>0.693125</td>\n",
+              "      <td>0.730577</td>\n",
+              "      <td>0.661909</td>\n",
+              "      <td>0.683394</td>\n",
+              "      <td>0.624376</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Batches:   0%|          | 0/88 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1f82319d44eb4a34a919a54a38bf6549"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Corpus Chunks: 100%|██████████| 1/1 [00:41<00:00, 41.93s/it]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ace7a954f0544ca1b9cd19f56603f5a5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Batches:   0%|          | 0/88 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "03934ff1821c4bedbf8eaa87c26e1f70"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Corpus Chunks: 100%|██████████| 1/1 [00:42<00:00, 42.03s/it]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Batches:   0%|          | 0/88 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9b370c30602e4179ba03c1e26cb54090"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\rCorpus Chunks:   0%|          | 0/1 [00:00<?, ?it/s]"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -qqq datasets\n",
+        "!pip install -qqq transformers==4.45.2 sentence-transformers==3.1.1\n",
+        "\n",
+        "from sentence_transformers import SentenceTransformer, InputExample, losses\n",
+        "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
+        "from torch.utils.data import DataLoader\n",
+        "import datasets\n",
+        "from huggingface_hub import login\n",
+        "\n",
+        "# Step 1: Log in to Hugging Face Hub\n",
+        "huggingface_token = \"token\"  # Replace with your token\n",
+        "login(token=huggingface_token)\n",
+        "\n",
+        "# Load the pre-trained model\n",
+        "model_id = \"dunzhang/stella_en_400M_v5\"\n",
+        "model = SentenceTransformer(model_id, trust_remote_code=True)\n",
+        "\n",
+        "# Load the dataset\n",
+        "obliqa_dataset = datasets.load_dataset(\"DrishtiSharma/obliqa\")\n",
+        "\n",
+        "# Constants\n",
+        "BATCH_SIZE = 10\n",
+        "EPOCHS = 5\n",
+        "\n",
+        "# Prepare the training data\n",
+        "train_dataset = obliqa_dataset['train']\n",
+        "corpus = {}\n",
+        "queries = {}\n",
+        "relevant_docs = {}\n",
+        "examples = []\n",
+        "\n",
+        "for row in train_dataset:\n",
+        "    query_id = row['QuestionID']\n",
+        "    query = row['Question']\n",
+        "    passages = row['Passages']\n",
+        "\n",
+        "    queries[query_id] = query\n",
+        "\n",
+        "    for passage in passages:\n",
+        "        passage_id = passage['PassageID']\n",
+        "        corpus[passage_id] = passage['Passage']\n",
+        "\n",
+        "        if query_id not in relevant_docs:\n",
+        "            relevant_docs[query_id] = []\n",
+        "        relevant_docs[query_id].append(passage_id)\n",
+        "\n",
+        "for query_id, query in queries.items():\n",
+        "    if query_id in relevant_docs and relevant_docs[query_id]:\n",
+        "        passage_id = relevant_docs[query_id][0]\n",
+        "        text = corpus[passage_id]\n",
+        "        examples.append(InputExample(texts=[query, text]))\n",
+        "\n",
+        "# DataLoader and loss setup\n",
+        "train_loader = DataLoader(examples, batch_size=BATCH_SIZE, shuffle=True)\n",
+        "loss = losses.MultipleNegativesRankingLoss(model)\n",
+        "\n",
+        "# Prepare validation data\n",
+        "val_dataset = obliqa_dataset['validation']\n",
+        "val_corpus = {}\n",
+        "val_queries = {}\n",
+        "val_relevant_docs = {}\n",
+        "\n",
+        "for row in val_dataset:\n",
+        "    query_id = row['QuestionID']\n",
+        "    query = row['Question']\n",
+        "    passages = row['Passages']\n",
+        "\n",
+        "    val_queries[query_id] = query\n",
+        "\n",
+        "    for passage in passages:\n",
+        "        passage_id = passage['PassageID']\n",
+        "        val_corpus[passage_id] = passage['Passage']\n",
+        "\n",
+        "        if query_id not in val_relevant_docs:\n",
+        "            val_relevant_docs[query_id] = []\n",
+        "        val_relevant_docs[query_id].append(passage_id)\n",
+        "\n",
+        "# Define evaluator\n",
+        "evaluator = InformationRetrievalEvaluator(\n",
+        "    queries=val_queries,\n",
+        "    corpus=val_corpus,\n",
+        "    relevant_docs=val_relevant_docs,\n",
+        "    show_progress_bar=True\n",
+        ")\n",
+        "\n",
+        "# Train the model\n",
+        "warmup_steps = int(len(train_loader) * EPOCHS * 0.1)\n",
+        "\n",
+        "model.fit(\n",
+        "    train_objectives=[(train_loader, loss)],\n",
+        "    evaluator=evaluator,\n",
+        "    epochs=EPOCHS,\n",
+        "    warmup_steps=warmup_steps,\n",
+        "    output_path=\"stella_en_400M_v5-obliqa-5-epochs\",\n",
+        "    evaluation_steps=200,\n",
+        "    show_progress_bar=True\n",
+        ")\n",
+        "\n",
+        "print(\"Training complete.\")\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fn3kdb1H5NMD"
+      },
+      "outputs": [],
+      "source": [
+        "# Push the model to Hugging Face Hub\n",
+        "model_name_on_hub = \"stella_en_400M_v5-obliqa-5-epochs\"  # Replace with your desired model name\n",
+        "model.push_to_hub(model_name_on_hub)\n",
+        "\n",
+        "print(f\"Model has been pushed to Hugging Face Hub under the name: {model_name_on_hub}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zVrMyuUUJB_M"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.10.12"
+    },
+    "accelerator": "GPU",
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "006a724e98cd493ca0ce77169a472148": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
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+              "IPY_MODEL_18120c9035104c05bb2c0f1b43939e20",
+              "IPY_MODEL_fa38d90c7ade4800b354cc6a6f3f4766"
+            ],
+            "layout": "IPY_MODEL_5dc762206db44de49341e399ec05545c"
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+            "_model_module_version": "1.5.0",
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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