andersoncliffb
/

abstracts_to_tweet_model

@@ -1,199 +1,40 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+base_model: google/t5-v1_1-base
+tags:
+- datadreamer
+- datadreamer-0.46.0
+- synthetic
+- mistral-tiny
+- mistral-tiny
+- text2text-generation
+pipeline_tag: text2text-generation
+widget:
+  - text: "In this paper, we introduce a novel learning framework for addressing inconsistencies and incompleteness inEffects of Pre-training Task Structure on Cross-lingual Transferof large-scale, multilingual machine reading comprehension (MRC) models. Our proposed method, termed Structured-MRC, employs a new task structure that strategically balances knowledge transfer and specialized information acquisition across languages. Rather than using one universal pre-training task, Structured-MRC synchronizes task-wise pre-training across related language pairs. This technique allows our models to effectively learn and transfer recurring patterns while avoiding overgeneralization. Comprehensive experiments are carried out on eight diverse languages from the XNLI, XNLG, MARC, and WikiMRC datasets, demonstrating that the Structured-MRC framework significantly outperforms state-of-the-art approaches in terms of consistency, comprehensibility, and generality. The insights gained from this study highlight the importance of structuring learning tasks for cross-lingual transfer in MRC, with implications for various NLP applications."
+    example_title: "Example 1"
+  - text: "Title: Unsupervised Multilingual Contextual Word Embeddings: Incorporating Morphological and Syntactic Features\n\nIn this paper, we propose an unsupervised approach for developing multilingual contextual word embeddings that capture both morphological and syntactic properties. The model framework, dubbed 'UniMorph Syn', targets 100 low-resource languages while accounting for diverse inflection patterns and structurally complex sentences. By employing morphological analyzers, POS taggers, and dependency parsers as pre-processing steps, we enhance the quality and comprehensiveness of contextual representations. We analyze the model's performance through cross-linguistic and cross-task evaluations using several datasets and benchmarks, obtaining promising results and outperforming relevant baselines. Our work showcases the potential of unsupervised, large-scale, both morphologically and syntactically-aware models for low-resource languages within a multilingual context."
+    example_title: "Example 2"
+  - text: "In this work, we present an innovative deep learning model for Natural Language Processing (NLP) tasks that leverages the transformer architecture supplemented with a robust pre-training strategy on vast unstructured data. Our model, Scored-Efficient Transformer (SET), excels in balancing efficiency with quality, achieving competitive and in some cases better performance than existing models while being significantly more computationally efficient.\n\nSET's unique feature is the introduction of a novel dynamic attention mechanism, which selectively accord focus to important contextual features, steadfastly reducing computational requirements without compromising the semantic understanding or performance. Furthermore, we explore a novel pre-training schema, named PseudoCorpus, which entails the creation of pseudo corpora from task-specific data, effectively tailoring the model to cater to a diverse range of NLP tasks with minimal need for task-specific fine-tuning.\n\nExperimental evaluations on benchmark datasets including GLUE, SQuAD, and STS-B demonstrate not only signs of consistent improvements in various NLP tasks for SET, but also highlight its remarkable generalization ability across various tasks. In summary, SET revolutionizes the NLP paradigm with a critical balance of efficiency, productivity, and efficacy, pushing the boundaries of potential applications in real-world scenarios."
+    example_title: "Example 3"
 ---
+# Model Card
+[Add more information here](https://huggingface.co/templates/model-card-example)
+## Example Usage
+```python3
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+tokenizer = AutoTokenizer.from_pretrained('andersoncliffb/abstracts_to_tweet_model', revision=None) # Load tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained('andersoncliffb/abstracts_to_tweet_model', revision=None) # Load model
+pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)
+inputs = ['In this paper, we introduce a novel learning framework for addressing inconsistencies and incompleteness inEffects of Pre-training Task Structure on Cross-lingual Transferof large-scale, multilingual machine reading comprehension (MRC) models. Our proposed method, termed Structured-MRC, employs a new task structure that strategically balances knowledge transfer and specialized information acquisition across languages. Rather than using one universal pre-training task, Structured-MRC synchronizes task-wise pre-training across related language pairs. This technique allows our models to effectively learn and transfer recurring patterns while avoiding overgeneralization. Comprehensive experiments are carried out on eight diverse languages from the XNLI, XNLG, MARC, and WikiMRC datasets, demonstrating that the Structured-MRC framework significantly outperforms state-of-the-art approaches in terms of consistency, comprehensibility, and generality. The insights gained from this study highlight the importance of structuring learning tasks for cross-lingual transfer in MRC, with implications for various NLP applications.']
+print(pipe(inputs, max_length=512, do_sample=False))
+```
+---
+This model was trained with a synthetic dataset with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json).