Create kalium_recommend

src/recommend/kalium_recommend/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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src/recommend/kalium_recommend/README.md

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+---
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: sentence-similarity
+---
+This AI is building from Bert-Base-Uncased. It's specialized in recommend ad based in user profile.
+It's building for hackathon.
+Author: Kauã Martins Barros
+github: kaubarros-24
+email: martinsbarroskaua@gmail.com

src/recommend/kalium_recommend/config.json

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+{
+  "_name_or_path": "kalium_recommend",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_epochs": 10,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

src/recommend/kalium_recommend/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cdbd933f1156b7d3984263f418c941809d95c133c511fe99b882bd5156e3ff49
+size 437955572

src/recommend/kalium_recommend/requirements.txt

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+torch
+transformers

src/recommend/kalium_recommend/special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

src/recommend/kalium_recommend/tokenizer_config.json

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

src/recommend/potas_recommend

@@ -1 +0,0 @@
-Subproject commit 4d8378a8f9b645ff9f87e1bdc9551dfa42279984

src/recommend/routes/ai_routes.py

@@ -3,7 +3,7 @@ import torch
 from fastapi import APIRouter
 from transformers import BertForSequenceClassification, BertTokenizer
 
-model_name = "src/recommend/potas_recommend"
+model_name = "src/recommend/kalium_recommend"
 model = BertForSequenceClassification.from_pretrained(model_name)
 tokenizer = BertTokenizer.from_pretrained(model_name)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")