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README.md

@@ -32,7 +32,7 @@ dataset_info:
     num_examples: 3
   download_size: 317560335
   dataset_size: 127269024
-- config_name: splitted
+- config_name: split
   features:
   - name: doc_key
     dtype: string
@@ -79,10 +79,10 @@ license: cc-by-sa-4.0
   <img src="assets/bookcoref.png" width="700">
 </div>
 <!-- Aggiungi nome degli autori, ACL 2025, link  -->
-This data repository contains the <span style="font-variant: small-caps;">BookCoref</span> dataset, introduced in the paper "<span style="font-variant: small-caps;">BookCoref</span>: Coreference Resolution at Book Scale" by <a href="https://arxiv.org/abs/2507.12075"> Martinelli et al. (2025)</a>, presented at the <a href="https://2025.aclweb.org/">ACL 2025</a> conference.
+This data repository contains the <span style="font-variant: small-caps;">BookCoref</span> dataset, introduced in the paper "<span style="font-variant: small-caps;">BookCoref</span>: Coreference Resolution at Book Scale" by <a href="https://arxiv.org/"> Martinelli et al. (2025)</a>, presented at the <a href="https://2025.aclweb.org/">ACL 2025</a> conference.
 
 We release both the manually-annotated `test` split (<span style="font-variant: small-caps;">BookCoref</span><sub>gold</sub>) and the pipeline-generated `train` and `validation` splits (<span style="font-variant: small-caps;">BookCoref</span><sub>silver</sub>).
-In order to enable the replication of our results, we also release the splitted version of each split as a separate configuration.
+In order to enable the replication of our results, we also release the split version of each split as a separate configuration.
 <!-- As specified in the paper, this version is obtained through chunking the text into contiguous windows of 1500 tokens, retaining the coreference clusters of each window. -->
 
 ## ⚠️ Project Gutenberg license disclaimer
@@ -98,7 +98,7 @@ Users are responsible for checking the copyright status of each book in their co
 To use the <span style="font-variant: small-caps;">BookCoref</span> dataset, you need to install the following Python packages in your environment:
 
 ```bash
-pip install "datasets<=3.6.0" "deepdiff==8.5.0" "spacy==3.8.7" "nltk==3.9.1"
+pip install "datasets<=3.6.0" deepdiff spacy nltk
 ```
 
 You can then load each configuration through Huggingface's `datasets` library:
@@ -107,7 +107,7 @@ You can then load each configuration through Huggingface's `datasets` library:
 from datasets import load_dataset
 
 bookcoref = load_dataset("sapienzanlp/bookcoref")
-bookcoref_splitted = load_dataset("sapienzanlp/bookcoref", name="splitted")
+bookcoref_split = load_dataset("sapienzanlp/bookcoref", name="split")
 ```
 
 These commands will download and preprocess the books, add the coreference annotations, and return a `DatasetDict` according to the requested configuration.
@@ -127,7 +127,7 @@ DatasetDict({
         num_rows: 3
     })
 })
->>> bookcoref_splitted
+>>> bookcoref_split
 DatasetDict({
     train: Dataset({
         features: ['doc_key', 'gutenberg_key', 'sentences', 'clusters', 'characters'],
@@ -144,8 +144,6 @@ DatasetDict({
 })
 ``` 
 
-### Local Download
-To locally download the dataset as a jsonlines file, follow the procedure on our [official GitHub repo](http://github.com/sapienzanlp/bookcoref).
 
 ## ℹ️ Data format