initial commit

.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+license: mit
+tags:
+- text-classification
+- topic-classification
+- small-model
+- synthetic-data
+- tanaos
+- artifex
+base_model:
+- FacebookAI/roberta-base
+datasets:
+- tanaos/synthetic-topic-classification-dataset-v1
+library_name: transformers
+---
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/tanaos/.github/master/assets/logo.png" width="250px" alt="Tanaos – Train task specific LLMs without training data, for offline NLP and Text Classification">
+</p>
+
+# tanaos-topic-classification-v1: A small but performant topic classification model
+
+This model was created by Tanaos with the [Artifex Python library](https://github.com/tanaos/artifex).
+
+This is a **topic classification model** based on [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) and fine-tuned on [a synthetic dataset](https://huggingface.co/datasets/tanaos/synthetic-topic-classification-dataset-v1) to classify text into one of 15 different intent categories:
+
+| Topic | Description |
+|--------|-------------|
+| `politics` | elections, policies, scandals, ideology. |
+| `health` | physical health, mental health, fitness, diets, medical advice. |
+| `technology` | gadgets, software, AI, cybersecurity. |
+| `entertainment` | movies, TV shows, music, celebrities, streaming platforms. |
+| `money_finance` | investing, budgeting, crypto, real estate. |
+| `relationships_dating` | romance, breakups, marriage, family drama. |
+| `education_learning` | schools, universities, self-study, online courses.,
+| `work_careers` | job hunting, workplace culture, remote work, career advice. |
+| `science` | research, space, climate, biology, physics, chemistry and the scientific method. |
+| `society_culture` | identity, inequality, norms, language, and society. |
+| `gaming` | video games, esports, hardware, mods, and gaming culture. |
+| `lifestyle_hobbies` | travel, food, fashion, DIY, productivity systems. |
+| `sports` | teams, athletes, events, scores, and sports culture. |
+| `automotive` | cars, motorcycles, reviews, maintenance, and industry news. |
+| `other` | miscellaneous topics not covered by the other categories. |
+
+## How to Use
+
+### Via the Artifex library (`pip install artifex`)
+
+```python
+from artifex import Artifex
+
+topic_classification = Artifex().topic_classification
+
+print(topic_classification("What do you think about the latest AI advancements?"))
+# >>> [{'label': 'technology', 'score': 0.9910}]
+```
+
+### Via the Transformers library
+
+```python
+from transformers import pipeline
+
+clf = pipeline("text-classification", model="tanaos/tanaos-topic-classification-v1")
+
+print(clf("What do you think about the latest AI advancements?"))
+# >>> [{'label': 'technology', 'score': 0.9910}]
+```
+
+## Model Description
+
+- **Base model:** `FacebookAI/roberta-base`
+- **Task:** Text classification (topic classification)
+- **Languages:** English
+- **Fine-tuning data:** A synthetic, custom dataset of 10,000 utterances, each belonging to one of 15 different topic categories.
+
+## Training Details
+
+This model was trained using the [Artifex Python library](https://github.com/tanaos/artifex)
+
+```bash
+pip install artifex
+```
+
+by providing the following instructions and generating 10,000 synthetic training samples:
+
+```python
+from artifex import Artifex
+
+
+topic_classification = Artifex().topic_classification
+
+topic_classification.train(
+    domain="general",
+    classes={
+        "politics": "elections, policies, scandals, ideology",
+        "health": "physical health, mental health, fitness, diets, medical advice.",
+        "technology": "gadgets, software, AI, cybersecurity.",
+        "entertainment": "movies, TV shows, music, celebrities, streaming platforms.",
+        "money_finance": "investing, budgeting, crypto, real estate.",
+        "relationships_dating": "romance, breakups, marriage, family drama.",
+        "education_learning": "schools, universities, self-study, online courses.",
+        "work_careers": "job hunting, workplace culture, remote work, career advice.",
+        "science": "research, space, climate, biology, physics, chemistry and the scientific method.",
+        "society_culture": "identity, inequality, norms, language, and society.",
+        "gaming": "video games, esports, hardware, mods, and gaming culture.",
+        "lifestyle_hobbies": "travel, food, fashion, DIY, productivity systems.",
+        "sports": "teams, athletes, events, scores, and sports culture.",
+        "automotive": "cars, motorcycles, reviews, maintenance, and industry news.",
+        "other": "miscellaneous topics not covered by the other categories."
+    },
+    num_samples=10000
+)
+```
+
+## Intended Uses
+
+This model is intended to:
+- Classify conversations, reviews, articles, or any text into one of the predefined topic categories.
+- Be used in applications such as chatbots, content categorization, and sentiment analysis.
+- Serve as a lightweight alternative for topic classification tasks.
+
+Not intended for:
+- Use cases requiring extremely high accuracy or domain-specific knowledge without further fine-tuning.

config.json

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+{
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "politics",
+    "1": "health",
+    "2": "technology",
+    "3": "entertainment",
+    "4": "money_finance",
+    "5": "relationships_dating",
+    "6": "education_learning",
+    "7": "work_careers",
+    "8": "science",
+    "9": "society_culture",
+    "10": "gaming",
+    "11": "lifestyle_hobbies",
+    "12": "sports",
+    "13": "automotive",
+    "14": "other"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "automotive": 13,
+    "education_learning": 6,
+    "entertainment": 3,
+    "gaming": 10,
+    "health": 1,
+    "lifestyle_hobbies": 11,
+    "money_finance": 4,
+    "other": 14,
+    "politics": 0,
+    "relationships_dating": 5,
+    "science": 8,
+    "society_culture": 9,
+    "sports": 12,
+    "technology": 2,
+    "work_careers": 7
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7668d593244fd9212f063cda17ccbfa7fe2f0283a04adc486568f113b8beb43
+size 498652812

notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b71a1322",
+   "metadata": {},
+   "source": [
+    "# Get started with `tanaos-topic-classification-v1`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "469712c1",
+   "metadata": {},
+   "source": [
+    "## Option 1 - Use through the [Artifex library](https://github.com/tanaos/artifex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4bfa886",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install artifex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a8f8ec7",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from artifex import Artifex\n",
+    "\n",
+    "topic_classification = Artifex().topic_classification\n",
+    "\n",
+    "print(topic_classification(\"What do you think about the latest AI advancements?\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afcc6d57",
+   "metadata": {},
+   "source": [
+    "## Option 2 - Use through the Transformers library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff2b44c9",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae5368d6",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "clf = pipeline(\"text-classification\", model=\"tanaos/tanaos-topic-classification-v1\")\n",
+    "\n",
+    "print(clf(\"What do you think about the latest AI advancements?\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}