{ "source_file": "0930", "source_platform": "Medium", "author": "Shashank Kapadia", "title_listed": "Domain Adaptation: Fine-Tune Pre-Trained NLP Models", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/", "publish_date": "2023-07-04", "corpus_stage": "", "notes": "", "id": "73787309c4c1", "collected_at": "2026-10-02T09:56:27", "title": "Domain Adaptation: Fine-Tune Pre-Trained NLP Models | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# Domain Adaptation: Fine-Tune Pre-Trained NLP Models\nA step-by-step guide to fine-tuning pre-trained NLP models for any domain\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nJuly 4, 20238 min read\n![Green and red dots and lines in a mesh pattern](../images/000.jpg)\nPhoto by Pietro Jeng on Unsplash \n_Preface: This article presents a summary of information about the given topic. It should not be considered original research. The information and code included in this article may be influenced by things I have read or seen in the past from various online articles, research papers, books, and open-source code._\n_Co-Author:_[_Billy Hines_](https://medium.com/u/f6bf67256839?source=post_page---user_mention--a06659ca6668---------------------------------------)\n## Table of contents\n * [Introduction](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Introduction)\n * [Theoretical framework](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Theoretical-framework)\n * [Data overview](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Data-overview)\n * [Starting point: The baseline model](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Starting-point)\n * [Fine-tuning the model](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Fine-tuning)\n * [Evaluating the results](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Evaluation)\n * [Closing thoughts](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Closing-thoughts)\n\n\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## Introduction\nIn today’s world, the availability of pre-trained NLP models has greatly simplified the interpretation of textual data using deep learning techniques. However, while these models excel in general tasks, they often lack adaptability to specific domains. This comprehensive guide aims to walk you through the process of fine-tuning pre-trained NLP models to achieve improved performance in a particular domain.\n### Motivation\nAlthough pre-trained NLP models like BERT and the Universal Sentence Encoder (USE) are effective in capturing linguistic intricacies, their performance in domain-specific applications can be limited due to the diverse range of datasets they are trained on. This limitation becomes evident when analyzing relationships within a specific domain.\nFor example, when working with employment data, we expect the model to recognize the closer proximity between the roles of ‘Data Scientist’ and ‘Machine Learning Engineer’, or the stronger association between ‘Python’ and ‘TensorFlow’. Unfortunately, general-purpose models often miss these nuanced relationships.\nThe table below demonstrates the discrepancies in the similarity obtained from a base multilingual USE model:\n![](../images/001.png)\nFig 1. Similarity score between two text vectors from base [MultiLingual Universal Sentence Encoder Model](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)\nTo address this issue, we can fine-tune pre-trained models with high-quality, domain-specific datasets. This adaptation process significantly enhances the model’s performance and precision, fully unlocking the potential of the NLP model.\n> When dealing with large pre-trained NLP models, it is advisable to initially deploy the base model and consider fine-tuning only if its performance falls short for the specific problem at hand.\nThis tutorial focuses on fine-tuning the Universal Sentence Encoder (USE) model using easily accessible open-source data.\n## Theoretical overview\nFine-tuning an ML model can be achieved through various strategies, such as supervised learning and reinforcement learning. In this tutorial, we will concentrate on a one(few)-shot learning approach combined with a Siamese architecture for the fine-tuning process.\n### Methodology\nIn this tutorial, we utilize a Siamese neural network, which is a specific type of Artificial Neural Network. This network leverages shared weights while simultaneously processing two distinct input vectors to compute comparable output vectors. Inspired by one-shot learning, this approach has proven to be particularly effective in capturing semantic similarity, although it may require longer training times and lack probabilistic output.\nA Siamese neural network creates an ‘embedding space’ where related concepts are positioned closely, enabling the model to better discern semantic relations.\n![](../images/body-ffb4f02ce32cfc46be79.png)\nFig 2. Siamese architecture to fine-tune pre-trained NLP model\n * **Twin branches and shared weights** : The architecture consists of two identical branches, each containing an embedding layer with shared weights. These dual branches handle two inputs simultaneously, either similar or dissimilar.\n * **Similarity and transformation** : The inputs are transformed into vector embeddings using the pre-trained NLP model. The architecture then calculates the similarity between the vectors. The similarity score, ranging between -1 and 1, quantifies the angular distance between the two vectors, serving as a metric for their semantic similarity.\n * **Contrastive loss and learning** : The model’s learning is guided by the “Contrastive Loss,” which is the difference between the expected output (similarity score from the training data) and the computed similarity. This loss guides the adjustment of the model’s weights to minimize the loss and enhance the quality of the learned embeddings.\n\n\nTo learn more about one(few)-shot learning, siamese architecture, and contrastive loss, refer to the following resources:\n * [A Gentle Introduction to Siamese Neural Networks Architecture](https://www.projectpro.io/article/siamese-neural-networks/718?source=post_page-----a06659ca6668---------------------------------------)\n * [What is one-shot learning?](https://bdtechtalks.com/2020/08/12/what-is-one-shot-learning/?source=post_page-----a06659ca6668---------------------------------------)\n * [Contrastive Loss Explained](https://towardsdatascience.com/contrastive-loss-explaned-159f2d4a87ec?source=post_page-----a06659ca6668---------------------------------------)\n\n\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/medium-articles/blob/master/natural-language-processing/embedding-models/domain_adaption_fine_tune_nlp_model.ipynb).\n## Data overview\nFor the fine-tuning of pre-trained NLP models using this method, the training data should consist of pairs of text strings accompanied by similarity scores between them.\nThe training data follows the format shown below:\n![](../images/body-31ac304c1a0644a1326b.png)\nFig 3. Sample Format for Training Data\nIn this tutorial, we use a dataset sourced from the [ESCO classification dataset](https://esco.ec.europa.eu/en), which has been transformed to generate similarity scores based on the relationships between different data elements.\n> Preparing the training data is a crucial step in the fine-tuning process. It is assumed that you have access to the required data and a method to transform it into the specified format. Since the focus of this article is to demonstrate the fine-tuning process, we will omit the details of how the data was generated using the ESCO dataset.\n> The ESCO dataset is available for developers to freely utilize as a foundation for various applications that offer services like autocomplete, suggestion systems, job search algorithms, and job matching algorithms. The dataset used in this tutorial has been transformed and provided as a sample, allowing unrestricted usage for any purpose.\nLet’s start by examining the training data:\npython\n\n```\nimport pandas as pd\n\n# Read the CSV file into a pandas DataFrame\ndata = pd.read_csv(\"./data/training_data.csv\")\n\n# Print head\ndata.head()\n```\n\n![](../images/body-709061fe2e773d26e7a7.png)\nFig 4. Sample data used for fine-tuning the model\n## Starting point: The baseline model\nTo begin, we establish the [multilingual universal sentence encoder](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3) as our baseline model. It is essential to set this baseline before proceeding with the fine-tuning process.\nFor this tutorial, we will use the STS benchmark and a sample similarity visualization as metrics to evaluate the changes and improvements achieved through the fine-tuning process.\n> The STS Benchmark dataset consists of English sentence pairs, each associated with a similarity score. During the model training process, we evaluate the model’s performance on this benchmark set. The persisted scores for each training run are the Pearson correlation between the predicted similarity scores and the actual similarity scores in the dataset.\n> These scores ensure that as the model is fine-tuned with our context-specific training data, it maintains some level of generalizability.\npython\n\n```\n# Loads the Universal Sentence Encoder Multilingual module from TensorFlow Hub.\nbase_model_url = \"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\"\nbase_model = tf.keras.Sequential([\n hub.KerasLayer(base_model_url,\n input_shape=[],\n dtype=tf.string,\n trainable=False)\n])\n\n# Defines a list of test sentences. These sentences represent various job titles.\ntest_text = ['Data Scientist', 'Data Analyst', 'Data Engineer',\n 'Nurse Practitioner', 'Registered Nurse', 'Medical Assistant',\n 'Social Media Manager', 'Marketing Strategist', 'Product Marketing Manager']\n\n# Creates embeddings for the sentences in the test_text list. \n# The np.array() function is used to convert the result into a numpy array.\n# The .tolist() function is used to convert the numpy array into a list, which might be easier to work with.\nvectors = np.array(base_model.predict(test_text)).tolist()\n\n# Calls the plot_similarity function to create a similarity plot.\nplot_similarity(test_text, vectors, 90, \"base model\")\n\n# Computes STS benchmark score for the base model\npearsonr = sts_benchmark(base_model)\nprint(\"STS Benachmark: \" + str(pearsonr))\n```\n\n![](../images/body-71c79790265b4248fdbb.png)\nFig 5. Similarity visualizations across test words\n> STS Benchmark (dev): 0.8325\n## Fine-tuning the model\nThe next step involves constructing the siamese model architecture using the baseline model and fine-tuning it with our domain-specific data.\npython\n\n```\n# Load the pre-trained word embedding model\nembedding_layer = hub.load(base_model_url)\n\n# Create a Keras layer from the loaded embedding model\nshared_embedding_layer = hub.KerasLayer(embedding_layer, trainable=True)\n\n# Define the inputs to the model\nleft_input = keras.Input(shape=(), dtype=tf.string)\nright_input = keras.Input(shape=(), dtype=tf.string)\n\n# Pass the inputs through the shared embedding layer\nembedding_left_output = shared_embedding_layer(left_input)\nembedding_right_output = shared_embedding_layer(right_input)\n\n# Compute the cosine similarity between the embedding vectors\ncosine_similarity = tf.keras.layers.Dot(axes=-1, normalize=True)(\n [embedding_left_output, embedding_right_output]\n)\n\n# Convert the cosine similarity to angular distance\npi = tf.constant(math.pi, dtype=tf.float32)\nclip_cosine_similarities = tf.clip_by_value(\n cosine_similarity, -0.99999, 0.99999\n)\nacos_distance = 1.0 - (tf.acos(clip_cosine_similarities) / pi)\n\n# Package the model\nencoder = tf.keras.Model([left_input, right_input], acos_distance)\n\n# Compile the model\nencoder.compile(\n optimizer=tf.keras.optimizers.Adam(\n learning_rate=0.00001,\n beta_1=0.9,\n beta_2=0.9999,\n epsilon=0.0000001,\n amsgrad=False,\n clipnorm=1.0,\n name=\"Adam\",\n ),\n loss=tf.keras.losses.MeanSquaredError(\n reduction=keras.losses.Reduction.AUTO, name=\"mean_squared_error\"\n ),\n metrics=[\n tf.keras.metrics.MeanAbsoluteError(),\n tf.keras.metrics.MeanAbsolutePercentageError(),\n ],\n)\n\n# Print the model summary\nencoder.summary()\n```\n\n![](../images/body-202dd8cdfda101c9caa5.png)\nFig 6. Model architecture for fine-tuning\n### Fit the model\npython\n\n```\n# Define early stopping callback\nearly_stop = keras.callbacks.EarlyStopping(\n monitor=\"loss\", patience=3, min_delta=0.001\n)\n\n# Define TensorBoard callback\nlogdir = os.path.join(\".\", \"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\ntensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)\n\n# Model Input\nleft_inputs, right_inputs, similarity = process_model_input(data)\n\n# Train the encoder model\nhistory = encoder.fit(\n [left_inputs, right_inputs],\n similarity,\n batch_size=8,\n epochs=20,\n validation_split=0.2,\n callbacks=[early_stop, tensorboard_callback],\n)\n\n# Define model input\ninputs = keras.Input(shape=[], dtype=tf.string)\n\n# Pass the input through the embedding layer\nembedding = hub.KerasLayer(embedding_layer)(inputs)\n\n# Create the tuned model\ntuned_model = keras.Model(inputs=inputs, outputs=embedding)\n```\n\n## Evaluation\nNow that we have the fine-tuned model, let’s re-evaluate it and compare the results to those of the base model.\npython\n\n```\n# Creates embeddings for the sentences in the test_text list. \n# The np.array() function is used to convert the result into a numpy array.\n# The .tolist() function is used to convert the numpy array into a list, which might be easier to work with.\nvectors = np.array(tuned_model.predict(test_text)).tolist()\n\n# Calls the plot_similarity function to create a similarity plot.\nplot_similarity(test_text, vectors, 90, \"tuned model\")\n\n# Computes STS benchmark score for the tuned model\npearsonr = sts_benchmark(tuned_model)\nprint(\"STS Benachmark: \" + str(pearsonr))\n```\n\n![](../images/body-af8d6efee7bc40ca919f.png)\n> STS Benchmark (dev): 0.8349\nBased on fine-tuning the model on the relatively small dataset, the STS benchmark score is comparable to that of the baseline model, indicating that the tuned model still exhibits generalizability. However, the similarity visualization demonstrates strengthened similarity scores between similar titles and a reduction in scores for dissimilar ones.\n## Closing thoughts\nFine-tuning pre-trained NLP models for domain adaptation is a powerful technique to improve their performance and precision in specific contexts. By utilizing quality, domain-specific datasets and leveraging siamese neural networks, we can enhance the model’s ability to capture semantic similarity.\nThis tutorial provided a step-by-step guide to the fine-tuning process, using the Universal Sentence Encoder (USE) model as an example. We explored the theoretical framework, data preparation, baseline model evaluation, and the actual fine-tuning process. The results demonstrated the effectiveness of fine-tuning in strengthening similarity scores within a domain.\nBy following this approach and adapting it to your specific domain, you can unlock the full potential of pre-trained NLP models and achieve better results in your natural language processing tasks.\nThanks for reading. _If you have any feedback, please feel free to reach out by messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _or shooting me an email (smhkapadia[at]gmail.com)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# Domain Adaptation: Fine-Tune Pre-Trained NLP Models\nA step-by-step guide to fine-tuning pre-trained NLP models for any domain\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nJuly 4, 20238 min read\n![Green and red dots and lines in a mesh pattern](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation.jpg)\nPhoto by Pietro Jeng on Unsplash \n_Preface: This article presents a summary of information about the given topic. It should not be considered original research. The information and code included in this article may be influenced by things I have read or seen in the past from various online articles, research papers, books, and open-source code._\n_Co-Author:_[_Billy Hines_](https://medium.com/u/f6bf67256839?source=post_page---user_mention--a06659ca6668---------------------------------------)\n## Table of contents\n * [Introduction](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Introduction)\n * [Theoretical framework](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Theoretical-framework)\n * [Data overview](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Data-overview)\n * [Starting point: The baseline model](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Starting-point)\n * [Fine-tuning the model](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Fine-tuning)\n * [Evaluating the results](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Evaluation)\n * [Closing thoughts](https://towardsdatascience.com/domain-adaption-fine-tune-pre-trained-nlp-models-a06659ca6668/#Closing-thoughts)\n\n\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## Introduction\nIn today’s world, the availability of pre-trained NLP models has greatly simplified the interpretation of textual data using deep learning techniques. However, while these models excel in general tasks, they often lack adaptability to specific domains. This comprehensive guide aims to walk you through the process of fine-tuning pre-trained NLP models to achieve improved performance in a particular domain.\n### Motivation\nAlthough pre-trained NLP models like BERT and the Universal Sentence Encoder (USE) are effective in capturing linguistic intricacies, their performance in domain-specific applications can be limited due to the diverse range of datasets they are trained on. This limitation becomes evident when analyzing relationships within a specific domain.\nFor example, when working with employment data, we expect the model to recognize the closer proximity between the roles of ‘Data Scientist’ and ‘Machine Learning Engineer’, or the stronger association between ‘Python’ and ‘TensorFlow’. Unfortunately, general-purpose models often miss these nuanced relationships.\nThe table below demonstrates the discrepancies in the similarity obtained from a base multilingual USE model:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-1.png)\nFig 1. Similarity score between two text vectors from base [MultiLingual Universal Sentence Encoder Model](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)\nTo address this issue, we can fine-tune pre-trained models with high-quality, domain-specific datasets. This adaptation process significantly enhances the model’s performance and precision, fully unlocking the potential of the NLP model.\n> When dealing with large pre-trained NLP models, it is advisable to initially deploy the base model and consider fine-tuning only if its performance falls short for the specific problem at hand.\nThis tutorial focuses on fine-tuning the Universal Sentence Encoder (USE) model using easily accessible open-source data.\n## Theoretical overview\nFine-tuning an ML model can be achieved through various strategies, such as supervised learning and reinforcement learning. In this tutorial, we will concentrate on a one(few)-shot learning approach combined with a Siamese architecture for the fine-tuning process.\n### Methodology\nIn this tutorial, we utilize a Siamese neural network, which is a specific type of Artificial Neural Network. This network leverages shared weights while simultaneously processing two distinct input vectors to compute comparable output vectors. Inspired by one-shot learning, this approach has proven to be particularly effective in capturing semantic similarity, although it may require longer training times and lack probabilistic output.\nA Siamese neural network creates an ‘embedding space’ where related concepts are positioned closely, enabling the model to better discern semantic relations.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-2.png)\nFig 2. Siamese architecture to fine-tune pre-trained NLP model\n * **Twin branches and shared weights** : The architecture consists of two identical branches, each containing an embedding layer with shared weights. These dual branches handle two inputs simultaneously, either similar or dissimilar.\n * **Similarity and transformation** : The inputs are transformed into vector embeddings using the pre-trained NLP model. The architecture then calculates the similarity between the vectors. The similarity score, ranging between -1 and 1, quantifies the angular distance between the two vectors, serving as a metric for their semantic similarity.\n * **Contrastive loss and learning** : The model’s learning is guided by the “Contrastive Loss,” which is the difference between the expected output (similarity score from the training data) and the computed similarity. This loss guides the adjustment of the model’s weights to minimize the loss and enhance the quality of the learned embeddings.\n\n\nTo learn more about one(few)-shot learning, siamese architecture, and contrastive loss, refer to the following resources:\n * [A Gentle Introduction to Siamese Neural Networks Architecture](https://www.projectpro.io/article/siamese-neural-networks/718?source=post_page-----a06659ca6668---------------------------------------)\n * [What is one-shot learning?](https://bdtechtalks.com/2020/08/12/what-is-one-shot-learning/?source=post_page-----a06659ca6668---------------------------------------)\n * [Contrastive Loss Explained](https://towardsdatascience.com/contrastive-loss-explaned-159f2d4a87ec?source=post_page-----a06659ca6668---------------------------------------)\n\n\nThe complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/medium-articles/blob/master/natural-language-processing/embedding-models/domain_adaption_fine_tune_nlp_model.ipynb).\n## Data overview\nFor the fine-tuning of pre-trained NLP models using this method, the training data should consist of pairs of text strings accompanied by similarity scores between them.\nThe training data follows the format shown below:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-3.png)\nFig 3. Sample Format for Training Data\nIn this tutorial, we use a dataset sourced from the [ESCO classification dataset](https://esco.ec.europa.eu/en), which has been transformed to generate similarity scores based on the relationships between different data elements.\n> Preparing the training data is a crucial step in the fine-tuning process. It is assumed that you have access to the required data and a method to transform it into the specified format. Since the focus of this article is to demonstrate the fine-tuning process, we will omit the details of how the data was generated using the ESCO dataset.\n> The ESCO dataset is available for developers to freely utilize as a foundation for various applications that offer services like autocomplete, suggestion systems, job search algorithms, and job matching algorithms. The dataset used in this tutorial has been transformed and provided as a sample, allowing unrestricted usage for any purpose.\nLet’s start by examining the training data:\npython\n\n```\nimport pandas as pd\n\n# Read the CSV file into a pandas DataFrame\ndata = pd.read_csv(\"./data/training_data.csv\")\n\n# Print head\ndata.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-4.png)\nFig 4. Sample data used for fine-tuning the model\n## Starting point: The baseline model\nTo begin, we establish the [multilingual universal sentence encoder](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3) as our baseline model. It is essential to set this baseline before proceeding with the fine-tuning process.\nFor this tutorial, we will use the STS benchmark and a sample similarity visualization as metrics to evaluate the changes and improvements achieved through the fine-tuning process.\n> The STS Benchmark dataset consists of English sentence pairs, each associated with a similarity score. During the model training process, we evaluate the model’s performance on this benchmark set. The persisted scores for each training run are the Pearson correlation between the predicted similarity scores and the actual similarity scores in the dataset.\n> These scores ensure that as the model is fine-tuned with our context-specific training data, it maintains some level of generalizability.\npython\n\n```\n# Loads the Universal Sentence Encoder Multilingual module from TensorFlow Hub.\nbase_model_url = \"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\"\nbase_model = tf.keras.Sequential([\n hub.KerasLayer(base_model_url,\n input_shape=[],\n dtype=tf.string,\n trainable=False)\n])\n\n# Defines a list of test sentences. These sentences represent various job titles.\ntest_text = ['Data Scientist', 'Data Analyst', 'Data Engineer',\n 'Nurse Practitioner', 'Registered Nurse', 'Medical Assistant',\n 'Social Media Manager', 'Marketing Strategist', 'Product Marketing Manager']\n\n# Creates embeddings for the sentences in the test_text list. \n# The np.array() function is used to convert the result into a numpy array.\n# The .tolist() function is used to convert the numpy array into a list, which might be easier to work with.\nvectors = np.array(base_model.predict(test_text)).tolist()\n\n# Calls the plot_similarity function to create a similarity plot.\nplot_similarity(test_text, vectors, 90, \"base model\")\n\n# Computes STS benchmark score for the base model\npearsonr = sts_benchmark(base_model)\nprint(\"STS Benachmark: \" + str(pearsonr))\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-5.png)\nFig 5. Similarity visualizations across test words\n> STS Benchmark (dev): 0.8325\n## Fine-tuning the model\nThe next step involves constructing the siamese model architecture using the baseline model and fine-tuning it with our domain-specific data.\npython\n\n```\n# Load the pre-trained word embedding model\nembedding_layer = hub.load(base_model_url)\n\n# Create a Keras layer from the loaded embedding model\nshared_embedding_layer = hub.KerasLayer(embedding_layer, trainable=True)\n\n# Define the inputs to the model\nleft_input = keras.Input(shape=(), dtype=tf.string)\nright_input = keras.Input(shape=(), dtype=tf.string)\n\n# Pass the inputs through the shared embedding layer\nembedding_left_output = shared_embedding_layer(left_input)\nembedding_right_output = shared_embedding_layer(right_input)\n\n# Compute the cosine similarity between the embedding vectors\ncosine_similarity = tf.keras.layers.Dot(axes=-1, normalize=True)(\n [embedding_left_output, embedding_right_output]\n)\n\n# Convert the cosine similarity to angular distance\npi = tf.constant(math.pi, dtype=tf.float32)\nclip_cosine_similarities = tf.clip_by_value(\n cosine_similarity, -0.99999, 0.99999\n)\nacos_distance = 1.0 - (tf.acos(clip_cosine_similarities) / pi)\n\n# Package the model\nencoder = tf.keras.Model([left_input, right_input], acos_distance)\n\n# Compile the model\nencoder.compile(\n optimizer=tf.keras.optimizers.Adam(\n learning_rate=0.00001,\n beta_1=0.9,\n beta_2=0.9999,\n epsilon=0.0000001,\n amsgrad=False,\n clipnorm=1.0,\n name=\"Adam\",\n ),\n loss=tf.keras.losses.MeanSquaredError(\n reduction=keras.losses.Reduction.AUTO, name=\"mean_squared_error\"\n ),\n metrics=[\n tf.keras.metrics.MeanAbsoluteError(),\n tf.keras.metrics.MeanAbsolutePercentageError(),\n ],\n)\n\n# Print the model summary\nencoder.summary()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-8.png)\nFig 6. Model architecture for fine-tuning\n### Fit the model\npython\n\n```\n# Define early stopping callback\nearly_stop = keras.callbacks.EarlyStopping(\n monitor=\"loss\", patience=3, min_delta=0.001\n)\n\n# Define TensorBoard callback\nlogdir = os.path.join(\".\", \"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\ntensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)\n\n# Model Input\nleft_inputs, right_inputs, similarity = process_model_input(data)\n\n# Train the encoder model\nhistory = encoder.fit(\n [left_inputs, right_inputs],\n similarity,\n batch_size=8,\n epochs=20,\n validation_split=0.2,\n callbacks=[early_stop, tensorboard_callback],\n)\n\n# Define model input\ninputs = keras.Input(shape=[], dtype=tf.string)\n\n# Pass the input through the embedding layer\nembedding = hub.KerasLayer(embedding_layer)(inputs)\n\n# Create the tuned model\ntuned_model = keras.Model(inputs=inputs, outputs=embedding)\n```\n\n## Evaluation\nNow that we have the fine-tuned model, let’s re-evaluate it and compare the results to those of the base model.\npython\n\n```\n# Creates embeddings for the sentences in the test_text list. \n# The np.array() function is used to convert the result into a numpy array.\n# The .tolist() function is used to convert the numpy array into a list, which might be easier to work with.\nvectors = np.array(tuned_model.predict(test_text)).tolist()\n\n# Calls the plot_similarity function to create a similarity plot.\nplot_similarity(test_text, vectors, 90, \"tuned model\")\n\n# Computes STS benchmark score for the tuned model\npearsonr = sts_benchmark(tuned_model)\nprint(\"STS Benachmark: \" + str(pearsonr))\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/Domain-adaptation-6.png)\n> STS Benchmark (dev): 0.8349\nBased on fine-tuning the model on the relatively small dataset, the STS benchmark score is comparable to that of the baseline model, indicating that the tuned model still exhibits generalizability. However, the similarity visualization demonstrates strengthened similarity scores between similar titles and a reduction in scores for dissimilar ones.\n## Closing thoughts\nFine-tuning pre-trained NLP models for domain adaptation is a powerful technique to improve their performance and precision in specific contexts. By utilizing quality, domain-specific datasets and leveraging siamese neural networks, we can enhance the model’s ability to capture semantic similarity.\nThis tutorial provided a step-by-step guide to the fine-tuning process, using the Universal Sentence Encoder (USE) model as an example. We explored the theoretical framework, data preparation, baseline model evaluation, and the actual fine-tuning process. The results demonstrated the effectiveness of fine-tuning in strengthening similarity scores within a domain.\nBy following this approach and adapting it to your specific domain, you can unlock the full potential of pre-trained NLP models and achieve better results in your natural language processing tasks.\nThanks for reading. _If you have any feedback, please feel free to reach out by messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _or shooting me an email (smhkapadia[at]gmail.com)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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