Bump dependencies, update version and readme

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ default_stages: [commit, push]
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -19,7 +19,7 @@ repos:
         exclude: LICENSE
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.2
+    rev: v0.1.7
     hooks:
       - id: ruff-format
       - id: ruff

README.md

@@ -262,7 +262,7 @@ If you use Inseq in your research we suggest to include a mention to the specifi
 
 ## Research using Inseq
 
-Inseq has been used in various research projects. A list of known publications that use Inseq to conduct interpretability analyses of generative models is shown below. If you know more, please let us know or submit a pull request (*last updated: May 2023*).
+Inseq has been used in various research projects. A list of known publications that use Inseq to conduct interpretability analyses of generative models is shown below. If you know more, please let us know or submit a pull request (*last updated: December 2023*).
 
 <details>
   <summary><b>2023</b></summary>
@@ -273,6 +273,7 @@ Inseq has been used in various research projects. A list of known publications t
     <li> <a href="https://arxiv.org/abs/2310.01188">Quantifying the Plausibility of Context Reliance in Neural Machine Translation</a> (Sarti et al., 2023)</li>
     <li> <a href="https://arxiv.org/abs/2310.12127">A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation</a> (Attanasio et al., 2023)</li>
     <li> <a href="https://arxiv.org/abs/2310.09820">Assessing the Reliability of Large Language Model Knowledge</a> (Wang et al., 2023)</li>
+    <li> <a href="https://aclanthology.org/2023.conll-1.18/">Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue</a> (Molnar et al., 2023)</li>
   </ol>
 
 </details>

docs/source/_static/inseq.js

@@ -1,4 +1,4 @@
-var curr_width = $(window).width();
+var curr_width = window.innerWidth;
 
 function resizeHtmlExamples() {
     var examples = document.getElementsByClassName("html-example");
@@ -23,7 +23,7 @@ function onLoad() {
 
 window.addEventListener("load", onLoad);
 window.onresize = function() {
-    var wwidth = $(window).width();
+    var wwidth = window.innerWidth;
     if( curr_width !== wwidth ){
         window.location.reload();
         curr_width = wwidth;

docs/source/conf.py

@@ -27,7 +27,7 @@
 # The short X.Y version
 version = "0.5"
 # The full version, including alpha/beta/rc tags
-release = "0.5.0.dev0"
+release = "0.5.0"
 
 
 # Prefix link to point to master, comment this during version release and uncomment below line

examples/inseq_tutorial.ipynb

@@ -86,7 +86,7 @@
     "\n",
     "[Inseq](https://github.com/inseq-team/inseq) ([Sarti et al., 2023](https://arxiv.org/abs/2302.13942)) is a toolkit based on the [🤗 Transformers](https//github.com/huggingface/transformers) and [Captum](https://captum.ai/docs/introduction) libraries for intepreting language generation models using feature attribution methods. Inseq allows you to analyze the behavior of a language generation model by computing the importance of each input token for each token in the generated output using the various categories of attribution methods like those described in the previous section (use `inseq.list_feature_attribution_methods()` to list all available methods). You can refer to the [Inseq paper](https://arxiv.org/abs/2302.13942) for an overview of the tool and some usage examples.\n",
     "\n",
-    "The current version of the library (v0.5.0, June 2023) supports all [`AutoModelForSeq2SeqLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM) (among others, [T5](https://huggingface.co/docs/transformers/model_doc/t5), [Bart](https://huggingface.co/docs/transformers/model_doc/bart) and all >1000 [MarianNMT](https://huggingface.co/docs/transformers/model_doc/marian) MT models) and [AutoModelForCausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) (among others, [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [Bloom](https://huggingface.co/docs/transformers/model_doc/bloom) and [LLaMa](https://huggingface.co/docs/transformers/model_doc/llama)), including advanced tools for custom attribution targets and post-processing of attribution matrices.\n",
+    "The current version of the library (v0.5.0, December 2023) supports all [`AutoModelForSeq2SeqLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSeq2SeqLM) (among others, [T5](https://huggingface.co/docs/transformers/model_doc/t5), [Bart](https://huggingface.co/docs/transformers/model_doc/bart) and all >1000 [MarianNMT](https://huggingface.co/docs/transformers/model_doc/marian) MT models) and [AutoModelForCausalLM](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) (among others, [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [Bloom](https://huggingface.co/docs/transformers/model_doc/bloom) and [LLaMa](https://huggingface.co/docs/transformers/model_doc/llama)), including advanced tools for custom attribution targets and post-processing of attribution matrices.\n",
     "\n",
     "The following code is a \"Hello world\" equivalent in Inseq: in three lines of code, an English-to-Italian machine translation model is loaded alongside an attribution method, attribution is performed, and results are visualized:"
    ]