Convert dataset to Parquet

COPYRIGHT

@@ -1,14 +0,0 @@
-Experiment resources related to the MuLMS corpus.
-Copyright (c) 2023 Robert Bosch GmbH
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU Affero General Public License as published
-by the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU Affero General Public License for more details.
-You should have received a copy of the GNU Affero General Public License
-along with this program. If not, see <https://www.gnu.org/licenses/>.

LICENSE_CORPUS

@@ -1,428 +0,0 @@
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README.md

@@ -1,122 +1,122 @@
 ---
 license: cc-by-sa-4.0
 language:
-  - en
+- en
 size_categories:
-  - 10K<n<100K
+- 10K<n<100K
 source_datasets:
-  - original
+- original
 task_categories:
-  - fill-mask
-  - token-classification
-  - text-classification
+- fill-mask
+- token-classification
+- text-classification
 task_ids:
-  - named-entity-recognition
-  - slot-filling
+- named-entity-recognition
+- slot-filling
 pretty_name: Multi-Layer Materials Science Corpus
 configs:
-  - config_name: MuLMS_Corpus
-    data_files:
-      - split: train
-        path: MuLMS_Corpus/train-*
-      - split: validation
-        path: MuLMS_Corpus/validation-*
-      - split: test
-        path: MuLMS_Corpus/test-*
-    default: true
-  - config_name: NER_Dependencies
-    data_files:
-      - split: train
-        path: NER_Dependencies/train-*
-      - split: validation
-        path: NER_Dependencies/validation-*
-      - split: test
-        path: NER_Dependencies/test-*
+- config_name: MuLMS_Corpus
+  data_files:
+  - split: train
+    path: MuLMS_Corpus/train-*
+  - split: validation
+    path: MuLMS_Corpus/validation-*
+  - split: test
+    path: MuLMS_Corpus/test-*
+  default: true
+- config_name: NER_Dependencies
+  data_files:
+  - split: train
+    path: NER_Dependencies/train-*
+  - split: validation
+    path: NER_Dependencies/validation-*
+  - split: test
+    path: NER_Dependencies/test-*
 dataset_info:
-  - config_name: MuLMS_Corpus
-    features:
-      - name: doc_id
-        dtype: string
-      - name: sentence
-        dtype: string
-      - name: tokens
-        sequence: string
-      - name: beginOffset
-        dtype: int32
-      - name: endOffset
-        dtype: int32
-      - name: AZ_labels
-        dtype: string
-      - name: Measurement_label
-        dtype: string
-      - name: NER_labels
-        sequence:
-          - name: text
-            dtype: string
-          - name: id
-            dtype: int32
-          - name: value
-            dtype: string
-          - name: begin
-            dtype: string
-          - name: end
-            dtype: string
-          - name: tokenIndices
-            sequence: int32
-      - name: NER_labels_BILOU
-        sequence: string
-      - name: relations
-        sequence:
-          - name: ne_id_gov
-            dtype: int32
-          - name: ne_id_dep
-            dtype: int32
-          - name: label
-            dtype: string
-      - name: docFileName
-        dtype: string
-      - name: data_split
-        dtype: string
-      - name: category
-        dtype: string
-    splits:
-      - name: train
-        num_bytes: 7319898
-        num_examples: 7538
-      - name: validation
-        num_bytes: 1499121
-        num_examples: 1532
-      - name: test
-        num_bytes: 1236358
-        num_examples: 1114
-    download_size: 2792635
-    dataset_size: 10055377
-  - config_name: NER_Dependencies
-    features:
-      - name: ID
-        dtype: int32
-      - name: sentence
-        dtype: string
-      - name: token_id
-        dtype: int32
-      - name: token_text
-        dtype: string
-      - name: NE_Dependencies
-        dtype: string
-      - name: data_split
-        dtype: string
-    splits:
-      - name: train
-        num_bytes: 50517495
-        num_examples: 216806
-      - name: validation
-        num_bytes: 9320669
-        num_examples: 42010
-      - name: test
-        num_bytes: 8450774
-        num_examples: 33921
-    download_size: 3139932
-    dataset_size: 68288938
+- config_name: MuLMS_Corpus
+  features:
+  - name: doc_id
+    dtype: string
+  - name: sentence
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: beginOffset
+    dtype: int32
+  - name: endOffset
+    dtype: int32
+  - name: AZ_labels
+    dtype: string
+  - name: Measurement_label
+    dtype: string
+  - name: NER_labels
+    sequence:
+    - name: text
+      dtype: string
+    - name: id
+      dtype: int32
+    - name: value
+      dtype: string
+    - name: begin
+      dtype: string
+    - name: end
+      dtype: string
+    - name: tokenIndices
+      sequence: int32
+  - name: NER_labels_BILOU
+    sequence: string
+  - name: relations
+    sequence:
+    - name: ne_id_gov
+      dtype: int32
+    - name: ne_id_dep
+      dtype: int32
+    - name: label
+      dtype: string
+  - name: docFileName
+    dtype: string
+  - name: data_split
+    dtype: string
+  - name: category
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 7319898
+    num_examples: 7538
+  - name: validation
+    num_bytes: 1499121
+    num_examples: 1532
+  - name: test
+    num_bytes: 1236358
+    num_examples: 1114
+  download_size: 2792635
+  dataset_size: 10055377
+- config_name: NER_Dependencies
+  features:
+  - name: ID
+    dtype: int32
+  - name: sentence
+    dtype: string
+  - name: token_id
+    dtype: int32
+  - name: token_text
+    dtype: string
+  - name: NE_Dependencies
+    dtype: string
+  - name: data_split
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 50517495
+    num_examples: 216806
+  - name: validation
+    num_bytes: 9320669
+    num_examples: 42010
+  - name: test
+    num_bytes: 8450774
+    num_examples: 33921
+  download_size: 3139932
+  dataset_size: 68288938
 ---
 
 # Dataset Card for MuLMS
@@ -128,17 +128,17 @@ dataset_info:
 
 ### Dataset Description
 
-The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas:
-"Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel".
+The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas: 
+"Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel". 
 It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
 
-- **Measurement Frames**: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
-  was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
-  sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
-- **Named Entities**: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
-- **Relations**: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
-  The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
-- **Argumentative Zones**: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
+* __Measurement Frames__: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
+was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
+sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
+* __Named Entities__: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
+* __Relations__: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
+The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
+* __Argumentative Zones__: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
   zones in MuLMS, which leads to a sentence-level classification task.
 
 You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
@@ -153,6 +153,8 @@ For dataset statistics, please refer to both papers listed below. There you can
 
 MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
 
+__Important:__ To use the dataset reader, please install the UIMA CAS Python reader _puima_ using the following command: `pip install git+https://github.com/annefried/puima.git`.
+
 ### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
@@ -209,7 +211,7 @@ HuggingFace datasets do not support these sub-splits, hence they must be loaded
 
 Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
 
-- `index`: Unique instance ID for each token.
+- `index`: Unique instance ID for each token. 
 - `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
 - `Sentence`: Sentence string
 - `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
@@ -222,52 +224,52 @@ Each instance in this config refers to one token and carries a copy of the entir
 
 For the different layers, the following labels are available:
 
-- **Measurement Frames**:
-  - `Measurement`
-  - `Qual_Measurement`
-- **Named Entities**:
-  - `MAT`
-  - `NUM`
-  - `VALUE`
-  - `UNIT`
-  - `PROPERTY`
-  - `FORM`
-  - `MEASUREMENT` (measurement frame-evoking trigger)
-  - `CITE`
-  - `SAMPLE`
-  - `TECHNIQUE`
-  - `DEV`
-  - `RANGE`
-  - `INSTRUMENT`
-- **Relations**:
-  - `hasForm`
-  - `measuresProperty`
-  - `usedAs`
-  - `propertyValue`
-  - `conditionProperty`
-  - `conditionSample`
-  - `conditionPropertyValue`
-  - `usesTechnique`
-  - `measuresPropertyValue`
-  - `usedTogether`
-  - `conditionEnv`
-  - `usedIn`
-  - `conditionInstrument`
-  - `takenFrom`
-  - `dopedBy`
-- **Argumentative Zones**:
-  - `Motivation`
-  - `Background`
-    - `PriorWork`
-  - `Experiment`
-    - `Preparation`
-    - `Characterization`
-  - `Explanation`
-  - `Results`
-  - `Conclusion`
-  - `Heading`
-  - `Caption`
-  - `Metadata`
+* __Measurement Frames__:
+  * `Measurement`
+  * `Qual_Measurement`
+* __Named Entities__:
+  * `MAT`
+  * `NUM`
+  * `VALUE`
+  * `UNIT`
+  * `PROPERTY`
+  * `FORM`
+  * `MEASUREMENT` (measurement frame-evoking trigger)
+  * `CITE`
+  * `SAMPLE`
+  * `TECHNIQUE`
+  * `DEV`
+  * `RANGE`
+  * `INSTRUMENT`
+* __Relations__:
+  * `hasForm`
+  * `measuresProperty`
+  * `usedAs`
+  * `propertyValue`
+  * `conditionProperty`
+  * `conditionSample`
+  * `conditionPropertyValue`
+  * `usesTechnique`
+  * `measuresPropertyValue`
+  * `usedTogether`
+  * `conditionEnv`
+  * `usedIn`
+  * `conditionInstrument`
+  * `takenFrom`
+  * `dopedBy`
+* __Argumentative Zones__:
+  * `Motivation`
+  * `Background`
+    * `PriorWork`
+  * `Experiment`
+    * `Preparation`
+    * `Characterization`
+  * `Explanation`
+  * `Results`
+  * `Conclusion`
+  * `Heading`
+  * `Caption`
+  * `Metadata`
 
 ## Dataset Creation
 
@@ -293,7 +295,7 @@ You can find all the authors for every document in this corpus in [MuLMS_Corpus_
 
 #### Annotation process
 
-The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated
+The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated 
 using [INCEpTION](https://inception-project.github.io/).
 
 #### Who are the annotators?
@@ -313,10 +315,9 @@ This dataset does not contain any personal, sensitive or private data. MuLMS bui
 If you use our software or dataset in your scientific work, please cite both papers:
 
 **BibTeX:**
-
 ```
 @misc{schrader2023mulms,
-      title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
+      title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain}, 
       author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
       year={2023},
       eprint={2310.15569},
@@ -377,12 +378,12 @@ The MuLMS corpus is released under the [CC BY-SA 4.0](https://creativecommons.or
 
 ## Dataset Card Authors
 
-- Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
-- Matteo Finco (Bosch Research)
-- Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
-- Felix Hildebrand (Bosch Research)
-- Annemarie Friedrich (University of Augsburg)
+* Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
+* Matteo Finco (Bosch Research)
+* Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
+* Felix Hildebrand (Bosch Research)
+* Annemarie Friedrich (University of Augsburg)
 
 ## Dataset Card Contact
 
-For all questions, please contact [Timo Schrader](mailto:timo.schrader@de.bosch.com).
+For all questions, please contact [Timo Schrader](mailto:timo.schrader@de.bosch.com).