update model card README.md

README.md

@@ -1,80 +1,91 @@
 ---
+license: cc-by-4.0
+tags:
+- generated_from_trainer
 datasets:
 - wikiann
-language:
-- pl
-pipeline_tag: token-classification
-license: mit
-widget:
-- text: "Jestem Krzysiek i pracuję w Ministerstwie Sportu"
-- text: "Wiktoria pracuje w Krakowie, na AGH"
-- text: "Nazywam się Grzegorz Jasiński, pochodzę ze Szczebrzeszyna"
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: herbert-base-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: pl
+      split: validation
+      args: pl
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8885878330430295
+    - name: Recall
+      type: recall
+      value: 0.905945803735859
+    - name: F1
+      type: f1
+      value: 0.8971828692395376
+    - name: Accuracy
+      type: accuracy
+      value: 0.9568532096363909
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
 # herbert-base-ner
 
+This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2006
+- Precision: 0.8886
+- Recall: 0.9059
+- F1: 0.8972
+- Accuracy: 0.9569
+
 ## Model description
 
-**herbert-base-ner** is a fine-tuned HerBERT model that can be used for **Named Entity Recognition** .
-It has been trained to recognize three types of entities: person (PER), location (LOC) and organization (ORG).
+More information needed
 
-Specifically, this model is an *allegro/herbert-base-cased* model that was fine-tuned on the Polish subset of *wikiann* dataset.
+## Intended uses & limitations
 
+More information needed
 
-## Intended uses & limitations
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.207         | 1.0   | 2500 | 0.1929          | 0.8566    | 0.8884 | 0.8722 | 0.9499   |
+| 0.1528        | 2.0   | 5000 | 0.1979          | 0.8807    | 0.9006 | 0.8905 | 0.9547   |
+| 0.1195        | 3.0   | 7500 | 0.2006          | 0.8886    | 0.9059 | 0.8972 | 0.9569   |
+
+
+### Framework versions
 
-#### How to use
-
-You can use this model with Transformers *pipeline* for NER.
-
-```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("pietruszkowiec/herbert-base-ner")
-model = AutoModelForTokenClassification.from_pretrained("pietruszkowiec/herbert-base-ner")
-
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-example = "Nazywam się Grzegorz Jasiński, pochodzę ze Szczebrzeszyna"
-
-ner_results = nlp(example)
-print(ner_results)
-```
-
-### BibTeX entry and citation info
-
-```
-@inproceedings{mroczkowski-etal-2021-herbert,
-    title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
-    author = "Mroczkowski, Robert  and
-      Rybak, Piotr  and
-      Wr{\\'o}blewska, Alina  and
-      Gawlik, Ireneusz",
-    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
-    month = apr,
-    year = "2021",
-    address = "Kiyv, Ukraine",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
-    pages = "1--10",
-}
-```
-```
-@inproceedings{pan-etal-2017-cross,
-    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
-    author = "Pan, Xiaoman  and
-      Zhang, Boliang  and
-      May, Jonathan  and
-      Nothman, Joel  and
-      Knight, Kevin  and
-      Ji, Heng",
-    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = jul,
-    year = "2017",
-    address = "Vancouver, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/P17-1178",
-    doi = "10.18653/v1/P17-1178",
-    pages = "1946--1958",
-    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
-}
-```
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3