FrancescoPeriti
/

Llama2Dictionary

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 # Llama2Dictionary
 <!-- Provide a quick summary of what the model is/does. -->
@@ -11,46 +20,22 @@ You will need to login with your hugginface token (```[HF-TOKEN]```, in the foll
 This model is fine-tuned on English datasets of sense definitions. Given a target word and a usage example, the model generates a sense definition for the target word in-context.
 You can find more details in the paper [Automatically Generated Definitions and their utility for Modeling Word Meaning](link) by Francesco Periti, David Alfter, Nina Tahmasebi.
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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 ## Uses
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-### Direct Use
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 ```python
 import torch
 import warnings
@@ -165,151 +150,10 @@ for row in dataset:
     print(f"Target: {row['target']}\nExample: {row['example']}\nSense definition: {row['definition']}")
 ```
-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-## How to Get Started with the Model
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-## Training Details
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+---
+license: cc-by-sa-4.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- text-generation-inference
+---
 # Llama2Dictionary
 <!-- Provide a quick summary of what the model is/does. -->
 This model is fine-tuned on English datasets of sense definitions. Given a target word and a usage example, the model generates a sense definition for the target word in-context.
 You can find more details in the paper [Automatically Generated Definitions and their utility for Modeling Word Meaning](link) by Francesco Periti, David Alfter, Nina Tahmasebi.
+The repository of our project is [https://github.com/FrancescoPeriti/LlamaDictionary](https://github.com/FrancescoPeriti/LlamaDictionary).
 ## Uses
+The model is designed for research purposes and is conceived to work like a dictionary.
+However, given a word and an example usage, users don't choose from a list of definitions (as in a traditional dictionary); instead, the model directly provides the sense definition for the word in-context.
+<!-- ### Direct Use -->
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- ### Downstream Use [optional]-->
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Bias, Risks, and Limitations
+The fine-tuning datasets were limited to English, and generated definitions may reflect biases and stereotypes inherent in the underlying language model.
+## How to Get Started with the Model
 ```python
 import torch
 import warnings
     print(f"Target: {row['target']}\nExample: {row['example']}\nSense definition: {row['definition']}")
 ```
 ## Citation [optional]
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
 [More Information Needed]