Initial commit

README.md

@@ -1,3 +1,95 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+tags:
+- punctuation
+license: mit
+datasets:
+- yelp_polarity
+metrics:
+- f1
+---
+# ✨ bert-restore-punctuation
+[![forthebadge](https://forthebadge.com/images/badges/gluten-free.svg)]()
+
+This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews). 
+
+The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
+
+This model is intended for direct use as a punctuation restoration model for the general English language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
+
+Model restores the following punctuations -- **[! ? . , - : ; ' ]**
+
+The model also restores the upper-casing of words.
+
+-----------------------------------------------
+## 🚋 Usage
+**Below is a quick way to get up and running with the model.**
+1. First, install the package.
+```bash
+pip install rpunct
+```
+2. Sample python code.
+```python
+from rpunct import RestorePuncts
+# The default language is 'english'
+rpunct = RestorePuncts()
+rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
+by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were
+a few atoms thick anything thicker would cause the electrons to scatter in ways that could not be disentangled now a team again led by david muller the samuel b eckert
+professor of engineering has bested its own record by a factor of two with an electron microscope pixel array detector empad that incorporates even more sophisticated
+3d reconstruction algorithms the resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves""")
+# Outputs the following:
+# In 2018, Cornell researchers built a high-powered detector that, in combination with an algorithm-driven process called Ptychography, set a world record by tripling the
+# resolution of a state-of-the-art electron microscope. As successful as it was, that approach had a weakness. It only worked with ultrathin samples that were a few atoms
+# thick. Anything thicker would cause the electrons to scatter in ways that could not be disentangled. Now, a team again led by David Muller, the Samuel B. 
+# Eckert Professor of Engineering, has bested its own record by a factor of two with an Electron microscope pixel array detector empad that incorporates even more
+# sophisticated 3d reconstruction algorithms. The resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves.
+```
+
+**This model works on arbitrarily large text in English language and uses GPU if available.**
+
+-----------------------------------------------
+## 📡 Training data
+
+Here is the number of product reviews we used for finetuning the model:
+
+| Language | Number of text samples|
+| -------- | ----------------- |
+| English  | 560,000           |
+
+We found the best convergence around _**3 epochs**_, which is what presented here and available via a download.
+
+-----------------------------------------------
+## 🎯 Accuracy
+The fine-tuned model obtained the following accuracy on 45,990 held-out text samples:
+
+| Accuracy | Overall F1 | Eval Support |
+| -------- | ---------------------- | ------------------- |
+| 91%  | 90%                 | 45,990
+
+Below is a breakdown of the performance of the model by each label:
+
+|  label    |   precision  |  recall | f1-score  | support|
+| --------- | -------------|-------- | ----------|--------|
+|     **!**    |   0.45       | 0.17    |  0.24     |  424   
+|     **!+Upper**    |   0.43       | 0.34    |  0.38     |   98   
+|     **'**    |   0.60       | 0.27    |  0.37     |   11   
+|    **,**    |   0.59       | 0.51    |  0.55     | 1522   
+|     **,+Upper**    |   0.52       | 0.50    |  0.51     |  239   
+|     **-**    |   0.00       | 0.00    |  0.00     |   18   
+|     **.**    |   0.69       | 0.84    |  0.75     | 2488   
+|     **.+Upper**    |   0.65       | 0.52    |  0.57     |  274   
+|     **:**    |   0.52       | 0.31    |  0.39     |   39   
+|     **:+Upper**    |   0.36       | 0.62    |  0.45     |   16   
+|     **;**    |   0.00       | 0.00    |  0.00     |   17   
+|     **?**    |   0.54       | 0.48    |  0.51     |   46   
+|     **?+Upper**    |   0.40       | 0.50    |  0.44     |    4   
+|     **none**    |   0.96       | 0.96    |  0.96     |35352   
+|     **Upper**    |   0.84       | 0.82    |  0.83     | 5442   
+
+-----------------------------------------------
+## ☕ Contact 
+Contact [Daulet Nurmanbetov](daulet.nurmanbetov@gmail.com) for questions, feedback and/or requests for similar models.
+
+-----------------------------------------------

config.json

@@ -0,0 +1,58 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model_args.json

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optimizer.pt

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pytorch_model.bin

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scheduler.pt

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "/home/ubuntu/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4", "name_or_path": "bert-base-uncased"}

training_args.bin

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