Philschmid
Models by this creator
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bart-large-cnn-samsum
236
The bart-large-cnn-samsum model is a transformer-based text summarization model trained using Amazon SageMaker and the Hugging Face Deep Learning container. It was fine-tuned on the SamSum dataset, which consists of conversational dialogues and their corresponding summaries. This model is similar to other text summarization models like bart_summarisation and flan-t5-base-samsum, which have also been fine-tuned on the SamSum dataset. However, the maintainer philschmid notes that the newer flan-t5-base-samsum model outperforms this BART-based model on the SamSum evaluation set. Model inputs and outputs The bart-large-cnn-samsum model takes conversational dialogues as input and generates concise summaries as output. The input can be a single string containing the entire conversation, and the output is a summarized version of the input. Inputs Conversational dialogue**: A string containing the full text of a conversation, with each participant's lines separated by newline characters. Outputs Summary**: A condensed, coherent summary of the input conversation, generated by the model. Capabilities The bart-large-cnn-samsum model is capable of generating high-quality summaries of conversational dialogues. It can identify the key points and themes of a conversation and articulate them in a concise, readable form. This makes the model useful for tasks like customer service, meeting notes, and other scenarios where summarizing conversations is valuable. What can I use it for? The bart-large-cnn-samsum model can be used in a variety of applications that involve summarizing conversational text. For example, it could be integrated into a customer service chatbot to provide concise summaries of customer interactions. It could also be used to generate meeting notes or highlight the main takeaways from team discussions. Things to try While the maintainer recommends trying the newer flan-t5-base-samsum model instead, the bart-large-cnn-samsum model can still be a useful tool for text summarization. Experiment with different input conversations and compare the model's performance to the recommended alternative. You may also want to explore fine-tuning the model on your own specialized dataset to see if it can be further improved for your specific use case.
Updated 5/27/2024
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flan-t5-base-samsum
81
The flan-t5-base-samsum model is a fine-tuned version of the google/flan-t5-base model on the samsum dataset. It achieves strong performance on text summarization tasks, with results including a loss of 1.3716, Rouge1 of 47.2358, Rouge2 of 23.5135, Rougel of 39.6266, and Rougelsum of 43.3458. Model inputs and outputs The flan-t5-base-samsum model takes in text data and generates summarized output text. It can be used for a variety of text-to-text tasks beyond just summarization. Inputs Text data to be summarized or transformed Outputs Summarized or transformed text data Capabilities The flan-t5-base-samsum model demonstrates strong capabilities in text summarization, able to concisely capture the key points of longer input text. It could be used for tasks like summarizing news articles, meeting notes, or other lengthy documents. What can I use it for? The flan-t5-base-samsum model could be useful for automating text summarization in a variety of business and research applications. For example, it could help teams quickly process and synthesize large amounts of information, or provide summaries for customer support agents to reference. The model's flexibility also means it could potentially be fine-tuned for other text-to-text tasks beyond just summarization. Things to try One interesting thing to try with the flan-t5-base-samsum model is using it for interactive summarization, where the user can provide feedback and the model can iteratively refine the summary. This could help ensure the most salient points are captured. Another idea is to use the model in a pipeline with other NLP components, such as topic modeling or sentiment analysis, to gain deeper insights from text data.
Updated 5/28/2024
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flan-t5-xxl-sharded-fp16
52
The flan-t5-xxl-sharded-fp16 model is a fork of the FLAN-T5 XXL model, which is a language model that has been fine-tuned on over 1000 additional tasks compared to the original T5 model. This model is able to perform well on a variety of natural language processing tasks, even when compared to much larger models like PaLM 62B. The maintainer, philschmid, has provided a custom handler.py as an example of how to use the T5-11B model with inference endpoints on a single NVIDIA A10G GPU. Model inputs and outputs The flan-t5-xxl-sharded-fp16 model is a text-to-text transformer, meaning it takes text as input and generates text as output. It has been trained on a diverse set of languages, including English, Spanish, Japanese, French, and many others. Inputs Text in any of the supported languages Outputs Text in any of the supported languages, generated based on the input Capabilities The FLAN-T5 models, including flan-t5-xxl-sharded-fp16, are capable of performing a wide range of natural language processing tasks, such as question answering, language translation, and text generation. They have been shown to outperform even much larger models on certain benchmarks, demonstrating the power of instruction fine-tuning to improve the performance and usability of language models. What can I use it for? The primary use cases for the flan-t5-xxl-sharded-fp16 model are in research on language models, including zero-shot and few-shot learning tasks, as well as advancing fairness and safety research. The model should not be used directly in any application without first assessing the safety and fairness concerns specific to the intended use case. Things to try One interesting aspect of the flan-t5-xxl-sharded-fp16 model is its ability to perform well on a diverse set of languages, even when compared to larger models. Researchers could explore the model's performance on cross-lingual tasks or investigate how the model's multilingual capabilities might be leveraged for applications that require translations or understanding of multiple languages.
Updated 5/28/2024
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instruct-igel-001
47
The instruct-igel-001 model is an instruction-tuned German large language model (LLM) developed by philschmid on top of the BigScience BLOOM model, which was adapted to German by Malte Ostendorff. The goal of this test was to explore the potential of the BLOOM architecture for language modeling tasks that require instruction-based responses. The model was fine-tuned using a dataset of naive translations of instruction-based content from English to German. While this approach may introduce errors in the translated content, the model was able to learn to generate instruction-based responses in German. However, the model also exhibits common deficiencies of language models, including hallucination, toxicity, and stereotypes. Similar German-focused language models include the EM German model family, which is based on the LeoLM Mistral architecture and offers versions in Llama2 7b, 13b and 70b as well as Mistral and LeoLM models. The DiscoLM German 7b v1 model is another Mistral-based German LLM focused on everyday use. Model inputs and outputs Inputs The instruct-igel-001 model takes in natural language text as input, similar to other large language models. Outputs The model generates natural language text as output, with a focus on instruction-based responses. Capabilities The instruct-igel-001 model is designed to provide accurate and reliable language understanding capabilities for a wide range of natural language understanding tasks, including sentiment analysis, language translation, and question answering. While the model exhibits some common deficiencies, it can be a useful tool for German language applications that require instruction-based responses. What can I use it for? The instruct-igel-001 model could be used for a variety of German language applications, such as: Automated assistants and chatbots that need to provide instruction-based responses Sentiment analysis and text classification for German language content Language translation between German and other languages The model could also be fine-tuned further on specific datasets or tasks to improve its performance. Things to try One interesting thing to try with the instruct-igel-001 model is to explore its capabilities and limitations around instruction-based responses. You could provide the model with a variety of German language instructions and observe how it responds, paying attention to any hallucinations, biases, or other issues that arise. This could help inform the development of future instruction-tuned German language models. Additionally, you could experiment with using the model for tasks like sentiment analysis or language translation, and compare its performance to other German language models to understand its strengths and weaknesses.
Updated 9/6/2024