diff --git "a/GEMMA_9B_B90_all_evals.ipynb" "b/GEMMA_9B_B90_all_evals.ipynb"
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+ "text/plain": [
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+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+ "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.7)\n",
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-nb7njmzo\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-nb7njmzo\n",
+ " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\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",
+ "Building wheels for collected packages: unsloth\n",
+ " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=d1fbbd2d814ca034a9932cbd8c2e32cd0cb3195060285f1b4253374d45a87c48\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-e3_bw86n/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.7\n",
+ " Uninstalling unsloth-2025.1.7:\n",
+ " Successfully uninstalled unsloth-2025.1.7\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
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+ "source": [
+ "!pip install datasets tqdm\n",
+ "!pip install unsloth\n",
+ "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
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+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "๐ฆฅ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+ "๐ฆฅ Unsloth Zoo will now patch everything to make training faster!\n",
+ "Unsloth: If you want to finetune Gemma 2, install flash-attn to make it faster!\n",
+ "To install flash-attn, do the below:\n",
+ "\n",
+ "pip install --no-deps --upgrade \"flash-attn>=2.6.3\"\n",
+ "==((====))== Unsloth 2025.1.7: Fast Gemma2 patching. Transformers: 4.47.1.\n",
+ " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
+ "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
+ " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
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+ "tokenizer.model: 0%| | 0.00/4.24M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "061357deee3b496397578997ffea146d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/636 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "83fc62e057224db4aa5dc207b251daff"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/34.4M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "22ae2c758b084051aeb9989a2fb752a0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Unsloth 2025.1.7 patched 42 layers with 42 QKV layers, 42 O layers and 42 MLP layers.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "PeftModelForCausalLM(\n",
+ " (base_model): LoraModel(\n",
+ " (model): Gemma2ForCausalLM(\n",
+ " (model): Gemma2Model(\n",
+ " (embed_tokens): Embedding(256000, 3584, padding_idx=0)\n",
+ " (layers): ModuleList(\n",
+ " (0-41): 42 x Gemma2DecoderLayer(\n",
+ " (self_attn): Gemma2Attention(\n",
+ " (q_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=4096, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=4096, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (k_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=2048, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=2048, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (v_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=2048, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=2048, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (o_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=4096, out_features=3584, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=4096, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=3584, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (rotary_emb): GemmaFixedRotaryEmbedding()\n",
+ " )\n",
+ " (mlp): Gemma2MLP(\n",
+ " (gate_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=14336, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=14336, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (up_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=14336, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=14336, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (down_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=14336, out_features=3584, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=14336, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=3584, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (act_fn): PytorchGELUTanh()\n",
+ " )\n",
+ " (input_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (post_attention_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (pre_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (post_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " )\n",
+ " )\n",
+ " (norm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " )\n",
+ " (lm_head): Linear(in_features=3584, out_features=256000, bias=False)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
+ "source": [
+ "from unsloth import FastLanguageModel\n",
+ "import pandas as pd\n",
+ "from datasets import load_dataset\n",
+ "import os\n",
+ "import torch\n",
+ "import torch.nn.functional as F\n",
+ "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+ "from tqdm import tqdm\n",
+ "tqdm.pandas()\n",
+ "max_seq_length = 2048\n",
+ "load_in_4bit = False\n",
+ "name = \"DrishtiSharma/GEMMA-9B-B90\"\n",
+ "model, tokenizer = FastLanguageModel.from_pretrained(model_name = name, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit,)\n",
+ "model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",], lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 3407, use_rslora = False, loftq_config = None,)\n",
+ "FastLanguageModel.for_inference(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**SINGLE TEST CASE**"
+ ],
+ "metadata": {
+ "id": "FICHwqm5aLUV"
+ },
+ "id": "FICHwqm5aLUV"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "input_text = \"เคเฅเคกเคผเฅเค 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\"\n",
+ "prompt = f\"### INPUT : {input_text} RESPONSE : \"\n",
+ "message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ "inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ "outputs = model.generate(input_ids=inputs, max_new_tokens=200, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n",
+ "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+ "processed_response = response.split(\"### RESPONSE :\\nmodel\")[-1].strip()\n",
+ "print(f\"Generated Response (20 tokens):\\n{processed_response}\\n\")\n",
+ "with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ "token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ "token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ "token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ "token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ "for i, score in enumerate(scores, 1):\n",
+ " probs = F.softmax(score, dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " print(f\"Probability of 'A' at token {i}: {prob_a:.4f}\")\n",
+ " print(f\"Probability of 'B' at token {i}: {prob_b:.4f}\")\n",
+ " print(f\"Probability of 'C' at token {i}: {prob_c:.4f}\")\n",
+ " print(f\"Probability of 'D' at token {i}: {prob_d:.4f}\")"
+ ],
+ "metadata": {
+ "id": "r1dozae-gO5B",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "79ba42c3-a2eb-4e71-e878-eb19ca43b289"
+ },
+ "id": "r1dozae-gO5B",
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Generated Response (20 tokens):\n",
+ "The correct answer is B) 700,003\n",
+ "\n",
+ "Probability of 'A' at token 1: 0.0133\n",
+ "Probability of 'B' at token 1: 0.0011\n",
+ "Probability of 'C' at token 1: 0.0005\n",
+ "Probability of 'D' at token 1: 0.0006\n",
+ "Probability of 'A' at token 2: 0.0000\n",
+ "Probability of 'B' at token 2: 0.0000\n",
+ "Probability of 'C' at token 2: 0.0000\n",
+ "Probability of 'D' at token 2: 0.0000\n",
+ "Probability of 'A' at token 3: 0.0000\n",
+ "Probability of 'B' at token 3: 0.0000\n",
+ "Probability of 'C' at token 3: 0.0000\n",
+ "Probability of 'D' at token 3: 0.0000\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE ENGLISH**"
+ ],
+ "metadata": {
+ "id": "7Al9PZfU2bhu"
+ },
+ "id": "7Al9PZfU2bhu"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "metadata": {
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "4909e09d-4620-42e1-dece-b898d63e4523"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 70.26706484641639\n",
+ "Max 'tok' value: 197\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 1172/1172 [04:40<00:00, 4.18it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 314\n",
+ "B 305\n",
+ "A 279\n",
+ "D 274\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8823\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_eng:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE HINDI**"
+ ],
+ "metadata": {
+ "id": "PubN4p-32_EC"
+ },
+ "id": "PubN4p-32_EC"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "mPFAiosJ3jzD",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "32b551c5-c3d4-4cef-bb00-a06078f10b56"
+ },
+ "id": "mPFAiosJ3jzD",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 114.31313993174061\n",
+ "Max 'tok' value: 389\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 1172/1172 [04:42<00:00, 4.15it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 303\n",
+ "C 303\n",
+ "A 302\n",
+ "D 264\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7867\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY ENGLISH**"
+ ],
+ "metadata": {
+ "id": "cT9I3npw43AP"
+ },
+ "id": "cT9I3npw43AP"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "6vmG3Z92410E",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "562f6e2c-56e0-4841-ac1b-55aca64c3b0f"
+ },
+ "id": "6vmG3Z92410E",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 60.46590909090909\n",
+ "Max 'tok' value: 193\n",
+ "Output\n",
+ "C 633\n",
+ "A 596\n",
+ "B 585\n",
+ "D 561\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 2376/2376 [09:30<00:00, 4.16it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 629\n",
+ "A 623\n",
+ "B 570\n",
+ "D 554\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9537\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY HINDI**"
+ ],
+ "metadata": {
+ "id": "A5dtJYX05T5v"
+ },
+ "id": "A5dtJYX05T5v"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "aFPK7wPX5TN7",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "eb21c6fd-9085-4ad8-b063-2c238f12d7a8"
+ },
+ "id": "aFPK7wPX5TN7",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 99.67003367003367\n",
+ "Max 'tok' value: 650\n",
+ "Output\n",
+ "C 610\n",
+ "A 570\n",
+ "B 563\n",
+ "D 535\n",
+ "4 26\n",
+ "1 26\n",
+ "3 23\n",
+ "2 22\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 2376/2376 [09:32<00:00, 4.15it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 650\n",
+ "C 618\n",
+ "B 569\n",
+ "D 539\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9019\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMLU ENGLISH**"
+ ],
+ "metadata": {
+ "id": "pFRbbDbE4Qui"
+ },
+ "id": "pFRbbDbE4Qui"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "FtThThQC8hs2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "cfa58b30-7903-42be-8451-6be369bdec2d"
+ },
+ "id": "FtThThQC8hs2",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 106.63509471585245\n",
+ "Max 'tok' value: 1009\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 14042/14042 [57:04<00:00, 4.10it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 3645\n",
+ "C 3599\n",
+ "D 3407\n",
+ "B 3391\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.6859\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMMLU HINDI**"
+ ],
+ "metadata": {
+ "id": "FeK3WGqS85al"
+ },
+ "id": "FeK3WGqS85al"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "wDxU0TXK85G7",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "56b7c587-278c-408b-b1f3-402bbf1aa53c"
+ },
+ "id": "wDxU0TXK85G7",
+ "execution_count": null,
+ "outputs": [
+ {
+ "metadata": {
+ "tags": null
+ },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 180.577624270047\n",
+ "Max 'tok' value: 1917\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ " 99%|โโโโโโโโโโ| 13844/14042 [55:45<00:46, 4.21it/s]"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ ENG**"
+ ],
+ "metadata": {
+ "id": "4aC98L-5Gi9D"
+ },
+ "id": "4aC98L-5Gi9D"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "m7ayEga9Ghkd",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "95b95237-2b60-40ce-aba8-926783488a88"
+ },
+ "id": "m7ayEga9Ghkd",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 138.53669724770643\n",
+ "Max 'tok' value: 1248\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 3270/3270 [13:19<00:00, 4.09it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2117\n",
+ "False 1153\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8685\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ HINDI**"
+ ],
+ "metadata": {
+ "id": "uAhhi93PHZ40"
+ },
+ "id": "uAhhi93PHZ40"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df = df[1:]\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "GOHy6uE285AB",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "0dce5239-011f-49c0-c106-1c5c05b7645c"
+ },
+ "id": "GOHy6uE285AB",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 250.7406727828746\n",
+ "Max 'tok' value: 22950\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 3269/3269 [13:11<00:00, 4.13it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2327\n",
+ "False 942\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8431\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ ENGLISH**"
+ ],
+ "metadata": {
+ "id": "1ugA-oyeReI9"
+ },
+ "id": "1ugA-oyeReI9"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "K4gKxj8ZRdYS",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "7790dd0d-5652-431c-afd8-555d1715a9d9"
+ },
+ "id": "K4gKxj8ZRdYS",
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 1000/1000 [04:04<00:00, 4.09it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 272\n",
+ "A 265\n",
+ "B 237\n",
+ "D 226\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8750\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ HINDI**"
+ ],
+ "metadata": {
+ "id": "JVW_cii1SR3c"
+ },
+ "id": "JVW_cii1SR3c"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "HrB5mDcf842y",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "34a34ff6-2384-42a2-d174-e76447ec480a"
+ },
+ "id": "HrB5mDcf842y",
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|โโโโโโโโโโ| 1000/1000 [04:07<00:00, 4.04it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 265\n",
+ "A 265\n",
+ "D 253\n",
+ "B 217\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7980\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**END**"
+ ],
+ "metadata": {
+ "id": "JqYw49CH3gfX"
+ },
+ "id": "JqYw49CH3gfX"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"BOOLQ ENGLISH : \" ,accuracy_boolq_eng)\n",
+ "print(\"BOOLQ HINDI : \" ,accuracy_boolq_hin)\n",
+ "print(\"C-MCQ ENGLISH : \" ,accuracy_mcq_eng)\n",
+ "print(\"C-MCQ HINDI : \" ,accuracy_mcq_hin)\n",
+ "print(\"MMMLU ENGLISH : \" ,accuracy_mmmlu_eng)\n",
+ "print(\"MMMLU HINDI : \" ,accuracy_mmmlu_hin)\n",
+ "print(\"ARC-E ENGLISH : \" ,accuracy_arc_e_eng)\n",
+ "print(\"ARC-E HINDI : \" ,accuracy_arc_e_hin)\n",
+ "print(\"ARC-C ENGLISH : \" ,accuracy_arc_c_eng)\n",
+ "print(\"ARC-C HINDI : \" ,accuracy_arc_c_hin)\n",
+ "avg_hin_acc = (accuracy_boolq_hin + accuracy_mcq_hin + accuracy_mmmlu_hin + accuracy_arc_e_hin + accuracy_arc_c_hin)/5\n",
+ "avg_eng_acc = (accuracy_boolq_eng + accuracy_mcq_eng + accuracy_mmmlu_eng + accuracy_arc_e_eng + accuracy_arc_c_eng)/5\n",
+ "print(\"AVG SCORE : HINDI : \" ,avg_hin_acc)\n",
+ "print(\"AVG SCORE : ENGLISH : \" ,avg_eng_acc)\n",
+ "avg_tot_acc = (avg_hin_acc + avg_eng_acc)/2\n",
+ "print(\"TOT AVG SCORE : \" ,avg_tot_acc)\n",
+ "print(\"CLICK CTRl+S and wait for 2 sec\")\n",
+ "name = name.split('/')[-1]\n",
+ "name = name + \".ipynb\"\n",
+ "print(\"1) NOTEBOOK NAME SHOULD BE : \", name)\n",
+ "print(\"2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \")\n",
+ "print(\"3) UPDATE THE GOOGLE SHEET WITH THE SCORES \")"
+ ],
+ "metadata": {
+ "id": "YI3SR_t1Vk2s",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "d10a9a9e-491b-44bc-e3f0-ced9ed1bc8d8"
+ },
+ "id": "YI3SR_t1Vk2s",
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BOOLQ ENGLISH : 0.8685015290519877\n",
+ "BOOLQ HINDI : 0.8430712756194555\n",
+ "C-MCQ ENGLISH : 0.875\n",
+ "C-MCQ HINDI : 0.798\n",
+ "MMMLU ENGLISH : 0.6859421734795613\n",
+ "MMMLU HINDI : 0.5370317618572853\n",
+ "ARC-E ENGLISH : 0.9537037037037037\n",
+ "ARC-E HINDI : 0.9019360269360269\n",
+ "ARC-C ENGLISH : 0.8822525597269625\n",
+ "ARC-C HINDI : 0.7866894197952219\n",
+ "AVG SCORE : HINDI : 0.7733456968415979\n",
+ "AVG SCORE : ENGLISH : 0.8530799931924431\n",
+ "TOT AVG SCORE : 0.8132128450170205\n",
+ "CLICK CTRl+S and wait for 2 sec\n",
+ "1) NOTEBOOK NAME SHOULD BE : GEMMA-9B-B90.ipynb\n",
+ "2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \n",
+ "3) UPDATE THE GOOGLE SHEET WITH THE SCORES \n"
+ ]
+ }
+ ]
+ },
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