Fix truncation bug + Update description

README.md

@@ -8,15 +8,6 @@ sdk_version: 1.2.0
 app_file: app.py
 pinned: false
 license: mit
-models:
-  - DeDeckerThomas/keyphrase-extraction-kbir-inspec
-  - DeDeckerThomas/keyphrase-extraction-distilbert-openkp
-  - DeDeckerThomas/keyphrase-extraction-distilbert-kptimes
-  - DeDeckerThomas/keyphrase-extraction-distilbert-inspec
-  - DeDeckerThomas/keyphrase-extraction-kbir-kpcrowd
-  - DeDeckerThomas/keyphrase-generation-keybart-inspec
-  - DeDeckerThomas/keyphrase-generation-t5-small-inspec
-  - DeDeckerThomas/keyphrase-generation-t5-small-openkp
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -35,7 +35,6 @@ def get_annotated_text(text, keyphrases, color="#d294ff"):
             rf"$K:{keyphrases.index(keyphrase)}\2",
             text,
             flags=re.I,
-            count=1,
         )
 
     result = []
@@ -131,8 +130,7 @@ from a text. Since this is a time-consuming process, Artificial Intelligence is
 Currently, classical machine learning methods, that use statistics and linguistics, are widely used
 for the extraction process. The fact that these methods have been widely used in the community has
 the advantage that there are many easy-to-use libraries. Now with the recent innovations in
-deep learning methods (such as recurrent neural networks and transformers, GANS, …),
-keyphrase extraction can be improved. These new methods also focus on the semantics and
+NLP, transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics and
 context of a document, which is quite an improvement.
 
 This space gives you the ability to test around with some keyphrase extraction and generation models.

pipelines/keyphrase_extraction_pipeline.py

@@ -11,7 +11,9 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
     def __init__(self, model, *args, **kwargs):
         super().__init__(
             model=AutoModelForTokenClassification.from_pretrained(model),
-            tokenizer=AutoTokenizer.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(
+                model, truncate=True
+            ),
             *args,
             **kwargs
         )

pipelines/keyphrase_generation_pipeline.py

@@ -8,7 +8,7 @@ class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
     def __init__(self, model, keyphrase_sep_token=";", *args, **kwargs):
         super().__init__(
             model=AutoModelForSeq2SeqLM.from_pretrained(model),
-            tokenizer=AutoTokenizer.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(model, truncate=True),
             *args,
             **kwargs
         )