Update README.md

README.md

@@ -1,47 +1,26 @@
 ---
 license: cc-by-4.0
-tags:
-- generated_from_trainer
 datasets:
 - wikiann
-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
+language:
+- pl
+pipeline_tag: token-classification
+widget:
+- text: "Jestem Krzysiek i pracuję w Ministerstwie Sportu"
+- text: "Wiktoria pracuje w Krakowie, na AGH"
+- text: "Nazywam się Grzegorz Jasiński, pochodzę ze Szczebrzeszyna"
 ---
 
-<!-- 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.
+## 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).
+
+Specifically, this model is an [*allegro/herbert-base-cased*](https://huggingface.co/allegro/herbert-base-cased) model that was fine-tuned on the Polish subset of *wikiann* dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.2006
 - Precision: 0.8886
@@ -49,43 +28,59 @@ It achieves the following results on the evaluation set:
 - F1: 0.8972
 - Accuracy: 0.9569
 
-## Model description
-
-More information needed
 
 ## Intended uses & limitations
 
-More information needed
-
-## 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
-
-- Transformers 4.29.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.12.0
-- Tokenizers 0.13.3
+#### 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.",
+}
+```