{
"cells": [
{
"cell_type": "markdown",
"id": "8cb392f0",
"metadata": {},
"source": [
"# Evaluate Classification"
]
},
{
"cell_type": "markdown",
"id": "81f7459d",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"id": "416c4176",
"metadata": {},
"source": [
"#### Load the Model and libaries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "51a7696a",
"metadata": {
"height": 115
},
"outputs": [],
"source": [
"from src.Language_Evaluation_LC import llm_language_evaluation\n",
"from src.data_analysis import run_analysis\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "f02b861d",
"metadata": {
"height": 30
},
"source": [
"#### Load the Constants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9686002a",
"metadata": {
"height": 47
},
"outputs": [],
"source": [
"PATH = 'data/full_dataset.csv'\n",
"MODEL = \"Mistral-7b\"\n",
"TEMPERATURE = 1\n",
"N_REPETITIONS = 11\n",
"REASONING = False\n",
"LANGUAGES = ['portuguese', 'spanish', 'english', 'tagalog']"
]
},
{
"cell_type": "markdown",
"id": "1c403d62",
"metadata": {},
"source": [
"#### Run The Experiments:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23ec69a1",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"The model file 'Models/Mistral-7b.gguf' already exists. Do you want to overwrite it? (yes/no): No\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model installation aborted.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 207: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 215: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 216: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 217: blk.24.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 224: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 225: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 229: blk.25.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 230: blk.25.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 231: blk.25.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 232: blk.25.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 233: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 234: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 235: blk.26.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 236: blk.26.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
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"llama_model_loader: - tensor 238: blk.26.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 239: blk.26.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 240: blk.26.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 241: blk.26.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 242: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 243: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 244: blk.27.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 245: blk.27.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 246: blk.27.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 247: blk.27.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 248: blk.27.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 249: blk.27.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 250: blk.27.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 251: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 252: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 253: blk.28.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 254: blk.28.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 255: blk.28.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 256: blk.28.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 257: blk.28.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 258: blk.28.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 259: blk.28.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 262: blk.29.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 268: blk.29.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 271: blk.30.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 274: blk.30.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 276: blk.30.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 277: blk.30.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 280: blk.31.attn_q.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 281: blk.31.attn_k.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 282: blk.31.attn_v.weight q8_0 [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 283: blk.31.attn_output.weight q8_0 [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 285: blk.31.ffn_up.weight q8_0 [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 286: blk.31.ffn_down.weight q8_0 [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 290: output.weight q8_0 [ 4096, 32000, 1, 1 ]\n",
"llama_model_loader: - kv 0: general.architecture str \n",
"llama_model_loader: - kv 1: general.name str \n",
"llama_model_loader: - kv 2: llama.context_length u32 \n",
"llama_model_loader: - kv 3: llama.embedding_length u32 \n",
"llama_model_loader: - kv 4: llama.block_count u32 \n",
"llama_model_loader: - kv 5: llama.feed_forward_length u32 \n",
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 \n",
"llama_model_loader: - kv 7: llama.attention.head_count u32 \n",
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 \n",
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 \n",
"llama_model_loader: - kv 10: llama.rope.freq_base f32 \n",
"llama_model_loader: - kv 11: general.file_type u32 \n",
"llama_model_loader: - kv 12: tokenizer.ggml.model str \n",
"llama_model_loader: - kv 13: tokenizer.ggml.tokens arr \n",
"llama_model_loader: - kv 14: tokenizer.ggml.scores arr \n",
"llama_model_loader: - kv 15: tokenizer.ggml.token_type arr \n",
"llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 \n",
"llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 \n",
"llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 \n",
"llama_model_loader: - kv 19: general.quantization_version u32 \n",
"llama_model_loader: - type f32: 65 tensors\n",
"llama_model_loader: - type q8_0: 226 tensors\n",
"llm_load_print_meta: format = GGUF V2 (latest)\n",
"llm_load_print_meta: arch = llama\n",
"llm_load_print_meta: vocab type = SPM\n",
"llm_load_print_meta: n_vocab = 32000\n",
"llm_load_print_meta: n_merges = 0\n",
"llm_load_print_meta: n_ctx_train = 32768\n",
"llm_load_print_meta: n_embd = 4096\n",
"llm_load_print_meta: n_head = 32\n",
"llm_load_print_meta: n_head_kv = 8\n",
"llm_load_print_meta: n_layer = 32\n",
"llm_load_print_meta: n_rot = 128\n",
"llm_load_print_meta: n_gqa = 4\n",
"llm_load_print_meta: f_norm_eps = 0.0e+00\n",
"llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n",
"llm_load_print_meta: n_ff = 14336\n",
"llm_load_print_meta: freq_base_train = 10000.0\n",
"llm_load_print_meta: freq_scale_train = 1\n",
"llm_load_print_meta: model type = 7B\n",
"llm_load_print_meta: model ftype = mostly Q8_0\n",
"llm_load_print_meta: model params = 7.24 B\n",
"llm_load_print_meta: model size = 7.17 GiB (8.50 BPW) \n",
"llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.1\n",
"llm_load_print_meta: BOS token = 1 ''\n",
"llm_load_print_meta: EOS token = 2 ''\n",
"llm_load_print_meta: UNK token = 0 ''\n",
"llm_load_print_meta: LF token = 13 '<0x0A>'\n",
"llm_load_tensors: ggml ctx size = 0.09 MB\n",
"llm_load_tensors: mem required = 7338.73 MB\n",
"...................................................................................................\n",
"llama_new_context_with_model: n_ctx = 2048\n",
"llama_new_context_with_model: freq_base = 10000.0\n",
"llama_new_context_with_model: freq_scale = 1\n",
"llama_new_context_with_model: kv self size = 256.00 MB\n",
"llama_new_context_with_model: compute buffer total size = 8.31 MB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"**************************************************\n",
"Question 1: \n",
"Language: portuguese\n",
"Question: \n",
"Em qual região ocular células caliciformes são fisiologicamente encontradas?\n",
"a)Córnea.\n",
"b)Limbo corneoescleral.\n",
"c)Linha cinzenta.\n",
"d)Prega semilunar.\n",
"Test #0: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #1: \n",
"{'response': 'a'}\n",
"Test #2: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #3: \n",
"except\n",
"except\n",
"{'response': 'c'}\n",
"Test #4: \n",
"except\n",
"{'response': 'a'}\n",
"Test #5: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #1: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #2: \n",
"{'response': 'a'}\n",
"Test #3: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'c'}\n",
"Test #4: \n",
"{'response': 'b'}\n",
"Test #5: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"except\n",
"{'response': 'a', 'explanation': 'A ordem das três denominações celulares encontradas no epitélio da córnea é, iniciando pelo mais superficial, seguido do intermediário e do profundo: Plana (Epitélio basal), Alada (Epitélio alado) e Basal (Epitélio basal)'}\n",
"Test #7: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'A'}\n",
"Test #8: \n",
"except\n",
"{'response': 'a'}\n",
"Test #9: \n",
"{'response': 'a'}\n",
"Test #10: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Language: spanish\n",
"Question: \n",
"Ordene los tres nombres de células que se encuentran en el epitelio corneal, comenzando con el má superficial, seguidos por el intermedio y lo profundo.\n",
"a) Plana, alada, basal.\n",
"b) Alada, basal, plana.\n",
"c) Basal, plana, alada.\n",
"d) Alada, plana, basal.\n",
"Test #0: \n",
"except\n",
"{'response': 'a'}\n",
"Test #1: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #2: \n",
"{'response': 'd'}\n",
"Test #3: \n",
"except\n",
"{'response': 'a'}\n",
"Test #4: \n",
"except\n",
"except\n",
"{'response': 'c'}\n",
"Test #5: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #7: \n",
"{'response': 'A'}\n",
"Test #8: \n",
"except\n",
"{'response': 'A'}\n",
"Test #9: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #10: \n",
"{'response': 'a'}\n",
"Language: english\n",
"Question: \n",
"Order the three cell names found in the corneal epithelium, starting with the most superficial, followed by the intermediate and the deep.\n",
"a) Flat, wing, basal.\n",
"b) wing, basal, flat.\n",
"c) Basal, flat, wing.\n",
"d) wing, flat, basal.\n",
"Test #0: \n",
"except\n",
"{'response': 'a'}\n",
"Test #1: \n",
"{'response': 'c'}\n",
"Test #2: \n",
"except\n",
"{'response': 'A'}\n",
"Test #3: \n",
"{'response': 'A'}\n",
"Test #4: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #5: \n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"{'response': 'a'}\n",
"Test #7: \n",
"except\n",
"except\n",
"{'response': 'A'}\n",
"Test #8: \n",
"{'response': 'a'}\n",
"Test #9: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #10: \n",
"except\n",
"{'response': 'A'}\n",
"Language: tagalog\n",
"Question: \n",
"Mag -order ng tatlong mga pangalan ng cell na matatagpuan sa corneal epithelium, na nagsisimula sa pinaka mababaw, na sinusundan ng intermediate at malalim.\n",
"a) Flat, wing, basal.\n",
"b) wing, basal, flat.\n",
"c) Basal, flat, wing.\n",
"d) wing, flat, basal.\n",
"Test #0: \n",
"{'response': 'wing, flat, basal.'}\n",
"Test #1: \n",
"{'response': 'd'}\n",
"Test #2: \n",
"except\n",
"{'response': 'A', 'description': 'In Portuguese, the cells found in the corneal epithelium starting from the outermost layer, intermediate and deep are called flat, wing, and basal respectively.'}\n",
"Test #3: \n",
"except\n",
"{'response': 'a'}\n",
"Test #4: \n",
"except\n",
"{'response': 'a'}\n",
"Test #5: \n",
"except\n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"except\n",
"{'response': 'a', 'explanation': 'The three cells in the corneal epithelium that are tagged starting from the base to the top and are linked to each other are flat, wing, and basal.'}\n",
"Test #7: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'c'}\n",
"Test #8: \n",
"except\n",
"{'response': 'b'}\n",
"Test #9: \n",
"{'response': 'b'}\n",
"Test #10: \n",
"{'response': 'A'}\n",
"**************************************************\n",
"**************************************************\n",
"Question 4: \n",
"Language: portuguese\n",
"Question: \n",
"Sobre a membrana de Descemet da córnea, é correto afirmar:\n",
"a)As células endoteliais não participam da sua formação.\n",
"b)Sua espessura no adulto é de cerca de 30 µm.\n",
"c)Sua porção mais anterior é de origem embrionária.\n",
"d)Sua espessura reduz-se com a idade.\n",
"Test #0: \n",
"{'response': 'b'}\n",
"Test #1: \n",
"{'response': 'b'}\n",
"Test #2: \n",
"except\n",
"except\n",
"{'response': 'b'}\n",
"Test #3: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'c', 'reasoning': \"The Descemet's membrane is formed from the lens vesicles in the embryo, not from endothelial cells.\"}\n",
"Test #4: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'b'}\n",
"Test #5: \n",
"except\n",
"{'response': 'b'}\n",
"Test #6: \n",
"{'response': 'd'}\n",
"Test #7: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a', 'description': 'As células endoteliais são as células que cobertem a parte vascular da membrana de Descemet da córnea. São elas as responsáveis pela produção dessa membrana.'}\n",
"Test #8: \n",
"except\n",
"{'response': 'b'}\n",
"Test #9: \n",
"{'response': 'a'}\n",
"Test #10: \n",
"except\n",
"except\n",
"{'response': 'B'}\n",
"Language: spanish\n",
"Question: \n",
"Con respecto a la membrana de la córnea de Descemet, es correcto declarar:\n",
"a) Las células endoteliales no participan en su formación.\n",
"b) Su grosor en el adulto es de aproximadamente 30 µm.\n",
"c) Su porción más anterior es de origen embrionario.\n",
"d) Su grosor se reduce con la edad.\n",
"Test #0: \n",
"{'response': 'b'}\n",
"Test #1: \n",
"{'response': 'b'}\n",
"Test #2: \n",
"{'response': 'a'}\n",
"Test #3: \n",
"except\n",
"{'response': 'b'}\n",
"Test #4: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'a) Las células endoteliales no participan en su formación.'}\n",
"Test #5: \n",
"except\n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #7: \n",
"except\n",
"except\n",
"{'response': 'a', 'explanation': 'Las células endoteliales no participan en su formación.'}\n",
"Test #8: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'b', 'question': 'Con respecto a la membrana de la córnea de Descemet, es correcto declarar: a) Las células endoteliales no participan en su formación. b) Su grosor en el adulto es de aproximadamente 30 µm. c) Su porción más anterior es de origen embrionario. d) Su grosor se reduce con la edad.'}\n",
"Test #9: \n",
"{'response': 'a'}\n",
"Test #10: \n",
"except\n",
"except\n",
"{'response': 'b'}\n",
"Language: english\n",
"Question: \n",
"Regarding Descemet's membrane of the cornea, it is correct to state:\n",
"a) Endothelial cells do not participate in its formation.\n",
"b) Its thickness in the adult is about 30 µm.\n",
"c) Its most anterior portion is of embryonic origin.\n",
"d) Its thickness reduces with age.\n",
"Test #0: \n",
"except\n",
"{'response': 'c'}\n",
"Test #1: \n",
"except\n",
"{'response': 'd'}\n",
"Test #2: \n",
"{'response': 'c'}\n",
"Test #3: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'A'}\n",
"Test #4: \n",
"{'response': 'B'}\n",
"Test #5: \n",
"{'response': 'a'}\n",
"Test #6: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #7: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #8: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #9: \n",
"except\n",
"{'response': 'c'}\n",
"Test #10: \n",
"{'response': 'a'}\n",
"Language: tagalog\n",
"Question: \n",
"Tungkol sa lamad ni Descemet ng kornea, tama itong sabihin:\n",
"a) Ang mga endothelial cells ay hindi nakikilahok sa pagbuo nito.\n",
"b) Ang kapal nito sa may sapat na gulang ay halos 30 µm.\n",
"c) ang pinaka -nauuna na bahagi nito ay mula sa embryonic na pinagmulan.\n",
"d) Ang kapal nito ay binabawasan sa edad.\n",
"Test #0: \n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #1: \n",
"{'response': 'a', 'explanation': \"Descemet's membrane is a specialized structure of the cornea that consists of cells called endothelial cells that do not allow blood to penetrate it.\"}\n",
"Test #2: \n",
"{'response': 'A'}\n",
"Test #3: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'c', 'description': 'Descemet`s membrane is the clear, avascular tissue that separates the iris from the anterior lens capsule. It develops from the embryonic mesoderm.'}\n",
"Test #4: \n",
"{'response': 'b', 'explanation': 'The thickness of the cornea varies with age and ranges from approximately 30 to 35 microns in young adults.'}\n",
"Test #5: \n",
"except\n",
"except\n",
"except\n",
"{'response': 'E'}\n",
"Test #6: \n",
"except\n",
"except\n",
"{'response': 'B'}\n",
"Test #7: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'a'}\n",
"Test #8: \n",
"except\n",
"except\n",
"except\n",
"except\n",
"{'response': 'c'}\n",
"Test #5: \n",
"{'response': 'C'}\n",
"Test #6: \n"
]
}
],
"source": [
"# Run evaluation:\n",
"llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES)"
]
},
{
"cell_type": "markdown",
"id": "3d6a1141",
"metadata": {},
"source": [
"#### See the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b331ddc",
"metadata": {
"height": 30
},
"outputs": [],
"source": [
"if N_REPETITIONS > 1:\n",
" df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv\")\n",
"else:\n",
" df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv\")\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "c276a11a",
"metadata": {},
"source": [
"### Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab1892c6",
"metadata": {},
"outputs": [],
"source": [
"TEMPERATURE = str(TEMPERATURE).replace('.', '_')\n",
"\n",
"run_analysis(model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, languages=LANGUAGES)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6e6ac9f",
"metadata": {},
"outputs": [],
"source": [
"N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
"pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6da1406a",
"metadata": {},
"outputs": [],
"source": [
"N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
"pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_by_theme_{MODEL}.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7955290d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:nlp_bias_vpython=3_8_15]",
"language": "python",
"name": "conda-env-nlp_bias_vpython_3_8_15-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}