mbley
/

german-webtext-quality-classifier-small

@@ -5,49 +5,68 @@ tags:
 - text-classification
 - generated_from_setfit_trainer
 widget:
-- text: 46 Abs. 2 BGG zum Beispiel die Schuldneranweisung gemäss den Bestimmungen
-    zum Schutz der ehelichen Gemeinschaft (Art. 177 ZGB; BGE 134 III 667), die Einsprache
-    gegen die Ausstellung einer Erbenbescheinigung (Art. 559 Abs. 1 ZGB; Urteil 5A_162/2007
-    vom 16. Juli 2007 E. 5.2) oder das Inventar über das Kindesvermögen (Art. 318
-    Abs. 2 ZGB; Urteil 5A_169/2007 vom 21. Juni 2007 E. 3).
-- text: Im OP der Kinderklinik der MHH werden pro Jahr zwischen 1500 und 2000 Operationen
-    durchgeführt.
 - text: Die Bindungen sollten anfangs in Fahrtrichtung zeigen.
 - text: Raumausstatter gesucht, Recklinghausen
 - text: Mehr Leistung durch Selbstgespräche
-metrics:
-- accuracy
 pipeline_tag: text-classification
 library_name: setfit
 inference: false
 ---
-# SetFit
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
-The model has been trained using an efficient few-shot learning technique that involves:
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
 ## Model Details
 ### Model Description
 - **Model Type:** SetFit
 <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
 - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
 - **Maximum Sequence Length:** 512 tokens
-<!-- - **Number of Classes:** Unknown -->
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
 ### Model Sources
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
 ## Uses
@@ -67,33 +86,9 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("setfit_model_id")
 # Run inference
-preds = model("Mehr Leistung durch Selbstgespräche")
 ```
-<!--
-### Downstream Use
-*List how someone could finetune this model on their own dataset.*
--->
-<!--
-### Out-of-Scope Use
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-<!--
-## Bias, Risks and Limitations
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-<!--
-### Recommendations
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
 ## Training Details
 ### Training Set Metrics
@@ -120,178 +115,6 @@ preds = model("Mehr Leistung durch Selbstgespräche")
 - eval_max_steps: -1
 - load_best_model_at_end: False
-### Training Results
-| Epoch  | Step  | Training Loss | Validation Loss |
-|:------:|:-----:|:-------------:|:---------------:|
-| 0.0001 | 1     | 3.2672        | -               |
-| 0.0119 | 100   | 5.7496        | -               |
-| 0.0239 | 200   | 4.7559        | -               |
-| 0.0358 | 300   | 4.2203        | -               |
-| 0.0477 | 400   | 4.0467        | -               |
-| 0.0596 | 500   | 3.9136        | -               |
-| 0.0716 | 600   | 3.791         | -               |
-| 0.0835 | 700   | 3.6316        | -               |
-| 0.0954 | 800   | 3.4742        | -               |
-| 0.1073 | 900   | 3.1001        | -               |
-| 0.1193 | 1000  | 2.4123        | -               |
-| 0.1312 | 1100  | 1.9843        | -               |
-| 0.1431 | 1200  | 1.9276        | -               |
-| 0.1551 | 1300  | 2.5268        | -               |
-| 0.1670 | 1400  | 2.229         | -               |
-| 0.1789 | 1500  | 2.0492        | -               |
-| 0.1908 | 1600  | 1.9396        | -               |
-| 0.2028 | 1700  | 1.6849        | -               |
-| 0.2147 | 1800  | 1.9385        | -               |
-| 0.2266 | 1900  | 1.6651        | -               |
-| 0.2385 | 2000  | 1.011         | -               |
-| 0.2505 | 2100  | 1.3135        | -               |
-| 0.2624 | 2200  | 1.347         | -               |
-| 0.2743 | 2300  | 1.4244        | -               |
-| 0.2863 | 2400  | 1.0954        | -               |
-| 0.2982 | 2500  | 0.9091        | -               |
-| 0.3101 | 2600  | 1.0739        | -               |
-| 0.3220 | 2700  | 0.9281        | -               |
-| 0.3340 | 2800  | 0.7909        | -               |
-| 0.3459 | 2900  | 0.5911        | -               |
-| 0.3578 | 3000  | 0.476         | -               |
-| 0.3698 | 3100  | 0.5782        | -               |
-| 0.3817 | 3200  | 0.4535        | -               |
-| 0.3936 | 3300  | 0.371         | -               |
-| 0.4055 | 3400  | 0.3692        | -               |
-| 0.4175 | 3500  | 0.2393        | -               |
-| 0.4294 | 3600  | 0.2623        | -               |
-| 0.4413 | 3700  | 0.2643        | -               |
-| 0.4532 | 3800  | 0.3065        | -               |
-| 0.4652 | 3900  | 0.2552        | -               |
-| 0.4771 | 4000  | 0.2093        | -               |
-| 0.4890 | 4100  | 0.217         | -               |
-| 0.5010 | 4200  | 0.1981        | -               |
-| 0.5129 | 4300  | 0.0827        | -               |
-| 0.5248 | 4400  | 0.1562        | -               |
-| 0.5367 | 4500  | 0.0438        | -               |
-| 0.5487 | 4600  | 0.0976        | -               |
-| 0.5606 | 4700  | 0.0307        | -               |
-| 0.5725 | 4800  | 0.0584        | -               |
-| 0.5844 | 4900  | 0.0503        | -               |
-| 0.5964 | 5000  | 0.0342        | -               |
-| 0.6083 | 5100  | 0.0244        | -               |
-| 0.6202 | 5200  | 0.0474        | -               |
-| 0.6322 | 5300  | 0.0346        | -               |
-| 0.6441 | 5400  | 0.0128        | -               |
-| 0.6560 | 5500  | 0.0077        | -               |
-| 0.6679 | 5600  | 0.0303        | -               |
-| 0.6799 | 5700  | 0.097         | -               |
-| 0.6918 | 5800  | 0.0152        | -               |
-| 0.7037 | 5900  | 0.0135        | -               |
-| 0.7156 | 6000  | 0.0222        | -               |
-| 0.7276 | 6100  | 0.0092        | -               |
-| 0.7395 | 6200  | 0.0277        | -               |
-| 0.7514 | 6300  | 0.0179        | -               |
-| 0.7634 | 6400  | 0.0092        | -               |
-| 0.7753 | 6500  | 0.0064        | -               |
-| 0.7872 | 6600  | 0.0176        | -               |
-| 0.7991 | 6700  | 0.0126        | -               |
-| 0.8111 | 6800  | 0.022         | -               |
-| 0.8230 | 6900  | 0.0187        | -               |
-| 0.8349 | 7000  | 0.0062        | -               |
-| 0.8469 | 7100  | 0.0031        | -               |
-| 0.8588 | 7200  | 0.0313        | -               |
-| 0.8707 | 7300  | 0.0026        | -               |
-| 0.8826 | 7400  | 0.0063        | -               |
-| 0.8946 | 7500  | 0.0008        | -               |
-| 0.9065 | 7600  | 0.0039        | -               |
-| 0.9184 | 7700  | 0.0009        | -               |
-| 0.9303 | 7800  | 0.001         | -               |
-| 0.9423 | 7900  | 0.0027        | -               |
-| 0.9542 | 8000  | 0.0023        | -               |
-| 0.9661 | 8100  | 0.0027        | -               |
-| 0.9781 | 8200  | 0.0022        | -               |
-| 0.9900 | 8300  | 0.0238        | -               |
-| 1.0019 | 8400  | 0.0008        | -               |
-| 1.0138 | 8500  | 0.0104        | -               |
-| 1.0258 | 8600  | 0.0014        | -               |
-| 1.0377 | 8700  | 0.0129        | -               |
-| 1.0496 | 8800  | 0.0014        | -               |
-| 1.0615 | 8900  | 0.002         | -               |
-| 1.0735 | 9000  | 0.0013        | -               |
-| 1.0854 | 9100  | 0.0046        | -               |
-| 1.0973 | 9200  | 0.0023        | -               |
-| 1.1093 | 9300  | 0.0023        | -               |
-| 1.1212 | 9400  | 0.0027        | -               |
-| 1.1331 | 9500  | 0.0021        | -               |
-| 1.1450 | 9600  | 0.0014        | -               |
-| 1.1570 | 9700  | 0.0036        | -               |
-| 1.1689 | 9800  | 0.0011        | -               |
-| 1.1808 | 9900  | 0.0027        | -               |
-| 1.1927 | 10000 | 0.0013        | -               |
-| 1.2047 | 10100 | 0.0007        | -               |
-| 1.2166 | 10200 | 0.0012        | -               |
-| 1.2285 | 10300 | 0.0033        | -               |
-| 1.2405 | 10400 | 0.0013        | -               |
-| 1.2524 | 10500 | 0.0008        | -               |
-| 1.2643 | 10600 | 0.0011        | -               |
-| 1.2762 | 10700 | 0.0007        | -               |
-| 1.2882 | 10800 | 0.0008        | -               |
-| 1.3001 | 10900 | 0.0005        | -               |
-| 1.3120 | 11000 | 0.0007        | -               |
-| 1.3240 | 11100 | 0.0015        | -               |
-| 1.3359 | 11200 | 0.0005        | -               |
-| 1.3478 | 11300 | 0.0011        | -               |
-| 1.3597 | 11400 | 0.001         | -               |
-| 1.3717 | 11500 | 0.0004        | -               |
-| 1.3836 | 11600 | 0.0015        | -               |
-| 1.3955 | 11700 | 0.0007        | -               |
-| 1.4074 | 11800 | 0.0007        | -               |
-| 1.4194 | 11900 | 0.0021        | -               |
-| 1.4313 | 12000 | 0.0004        | -               |
-| 1.4432 | 12100 | 0.0005        | -               |
-| 1.4552 | 12200 | 0.0007        | -               |
-| 1.4671 | 12300 | 0.0007        | -               |
-| 1.4790 | 12400 | 0.0015        | -               |
-| 1.4909 | 12500 | 0.0007        | -               |
-| 1.5029 | 12600 | 0.0004        | -               |
-| 1.5148 | 12700 | 0.0007        | -               |
-| 1.5267 | 12800 | 0.0017        | -               |
-| 1.5386 | 12900 | 0.0005        | -               |
-| 1.5506 | 13000 | 0.0006        | -               |
-| 1.5625 | 13100 | 0.0019        | -               |
-| 1.5744 | 13200 | 0.0004        | -               |
-| 1.5864 | 13300 | 0.0007        | -               |
-| 1.5983 | 13400 | 0.0005        | -               |
-| 1.6102 | 13500 | 0.0006        | -               |
-| 1.6221 | 13600 | 0.0003        | -               |
-| 1.6341 | 13700 | 0.0004        | -               |
-| 1.6460 | 13800 | 0.0003        | -               |
-| 1.6579 | 13900 | 0.0003        | -               |
-| 1.6698 | 14000 | 0.0006        | -               |
-| 1.6818 | 14100 | 0.0006        | -               |
-| 1.6937 | 14200 | 0.0003        | -               |
-| 1.7056 | 14300 | 0.0004        | -               |
-| 1.7176 | 14400 | 0.0003        | -               |
-| 1.7295 | 14500 | 0.0003        | -               |
-| 1.7414 | 14600 | 0.0003        | -               |
-| 1.7533 | 14700 | 0.0003        | -               |
-| 1.7653 | 14800 | 0.0004        | -               |
-| 1.7772 | 14900 | 0.0003        | -               |
-| 1.7891 | 15000 | 0.0003        | -               |
-| 1.8010 | 15100 | 0.0004        | -               |
-| 1.8130 | 15200 | 0.0004        | -               |
-| 1.8249 | 15300 | 0.0002        | -               |
-| 1.8368 | 15400 | 0.0003        | -               |
-| 1.8488 | 15500 | 0.0004        | -               |
-| 1.8607 | 15600 | 0.0003        | -               |
-| 1.8726 | 15700 | 0.0005        | -               |
-| 1.8845 | 15800 | 0.0004        | -               |
-| 1.8965 | 15900 | 0.0002        | -               |
-| 1.9084 | 16000 | 0.0002        | -               |
-| 1.9203 | 16100 | 0.0003        | -               |
-| 1.9323 | 16200 | 0.0003        | -               |
-| 1.9442 | 16300 | 0.0003        | -               |
-| 1.9561 | 16400 | 0.0004        | -               |
-| 1.9680 | 16500 | 0.0003        | -               |
-| 1.9800 | 16600 | 0.0002        | -               |
-| 1.9919 | 16700 | 0.0003        | -               |
 ### Framework Versions
 - Python: 3.10.4
 - SetFit: 1.1.2
@@ -316,21 +139,3 @@ preds = model("Mehr Leistung durch Selbstgespräche")
     copyright = {Creative Commons Attribution 4.0 International}
 }
 ```
-<!--
-## Glossary
-*Clearly define terms in order to be accessible across audiences.*
--->
-<!--
-## Model Card Authors
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-<!--
-## Model Card Contact
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->

 - text-classification
 - generated_from_setfit_trainer
 widget:
+- text: >-
+    46 Abs. 2 BGG zum Beispiel die Schuldneranweisung gemäss den Bestimmungen
+    zum Schutz der ehelichen Gemeinschaft (Art. 177 ZGB; BGE 134 III 667), die
+    Einsprache gegen die Ausstellung einer Erbenbescheinigung (Art. 559 Abs. 1
+    ZGB; Urteil 5A_162/2007 vom 16. Juli 2007 E. 5.2) oder das Inventar über das
+    Kindesvermögen (Art. 318 Abs. 2 ZGB; Urteil 5A_169/2007 vom 21. Juni 2007 E.
+    3).
+- text: >-
+    Im OP der Kinderklinik der MHH werden pro Jahr zwischen 1500 und 2000
+    Operationen durchgeführt.
 - text: Die Bindungen sollten anfangs in Fahrtrichtung zeigen.
 - text: Raumausstatter gesucht, Recklinghausen
 - text: Mehr Leistung durch Selbstgespräche
 pipeline_tag: text-classification
 library_name: setfit
 inference: false
+license: mit
+datasets:
+- mbley/german-webtext-quality-classification-dataset
+language:
+- de
+base_model:
+- distilbert/distilbert-base-german-cased
 ---
+# Bootstrapping a Sentence-Level Corpus Quality Classifier for Web Text using Active Learning (RANLP25)
+A multi-label sentence classifier trained with Active Learning for predicting high- or low-qality labels of german webtext.
+Training and evaluation code: <https://github.com/maximilian-bley/german-webtext-quality-classification>
 ## Model Details
+**Labels**
+- **0=Sentence Boundary:** Sentence boundary errors occur if the start or ending of a sentence is malformed. This is the case if it begins with a lower case letter or an atypical character, or lacks a proper terminal punctuation mark (e.g., period, exclamation mark, or question mark).
+- **1=Grammar Mistake:** Grammar mistakes are any grammatical errors such as incorrect articles, cases, word order and incorrect use or absence of words. Moreover, random-looking sequences of words, usually series of nouns, should be tagged. In most cases where this label is applicable, the sentence' comprehensibility or message is impaired.
+- **2=Spelling Anomaly:** A spelling anomaly is tagged when a word does not correspond to German spelling. This includes typos and incorrect capitalization (e.g. “all caps” or lower-case nouns). Spelling anomalies are irregularities that occur within the word boundary, meaning here text between two whitespaces. In particular, individual letters or nonsensical word fragments are also tagged.
+- **3=Punctuation Error:** Punctuation errors are tagged if a punctuation symbol has been placed incorrectly or is missing in the intended place. This includes comma errors, missing quotation marks or parentheses, periods instead of question marks or incorrect or missing dashes or hyphens.
+- **4=Non-linguistic Content:** Non-linguistic content includes all types of encoding errors, language-atypical occurrences of numbers and characters (e.g. random sequences of characters or letters), code (remnants), URLs, hashtags and emoticons.
+- **5=Letter Spacing:** Letter spacings are deliberately inserted spaces between the characters of a word.
+- **6=Clean:** Assigned if none of the other labels apply.
 ### Model Description
 - **Model Type:** SetFit
 <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
 - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
 - **Maximum Sequence Length:** 512 tokens
+**Number of Classes:** 6
+**Language:** German
 ### Model Sources
+- **Repository:**
+- **Paper:**
 ## Uses
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("setfit_model_id")
 # Run inference
+preds = model("在 Greding 出 口 离 开 A9 高 速 公 路 。")
 ```
 ## Training Details
 ### Training Set Metrics
 - eval_max_steps: -1
 - load_best_model_at_end: False
 ### Framework Versions
 - Python: 3.10.4
 - SetFit: 1.1.2
     copyright = {Creative Commons Attribution 4.0 International}
 }
 ```