Milan Straka commited on
Commit
febc85f
1 Parent(s): dc25bdd

Initial upload with a fixed tokenizer.

Browse files

The tokenizer now does not map several tokens to the same ID, and it
contains no holes.

Files changed (9) hide show
  1. LICENSE +438 -0
  2. README.md +151 -0
  3. config.json +22 -0
  4. merges.txt +0 -0
  5. pytorch_model.bin +3 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +1 -0
  9. vocab.json +0 -0
LICENSE ADDED
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README.md CHANGED
@@ -1,3 +1,154 @@
 
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  ---
 
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  license: cc-by-nc-sa-4.0
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
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  ---
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+ language: cs
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  license: cc-by-nc-sa-4.0
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+ tags:
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+ - RobeCzech
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+ - Czech
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+ - RoBERTa
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+ - ÚFAL
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  ---
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+
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+ # Model Card for RobeCzech
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+
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+ **If you are having issues with the tokenizer, please see https://huggingface.co/ufal/robeczech-base/discussions/4#64b8f6a7f1f8e6ea5860b314.**
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+
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+ # Model Details
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+
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+ ## Model Description
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+
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+ RobeCzech is a monolingual RoBERTa language representation model trained on Czech data.
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+
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+ - **Developed by:** Institute of Formal and Applied Linguistics, Charles University, Prague (UFAL)
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+ - **Shared by:** Hugging Face and [LINDAT/CLARIAH-CZ](https://hdl.handle.net/11234/1-3691)
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+ - **Model type:** Fill-Mask
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+ - **Language(s) (NLP):** cs
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+ - **License:** cc-by-nc-sa-4.0
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+ - **Model Architecture:** RoBERTa
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+ - **Resources for more information:**
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+ - [RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model](https://doi.org/10.1007/978-3-030-83527-9_17)
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+ - [arXiv preprint is also available](https://arxiv.org/abs/2105.11314)
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+
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+
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+ # Uses
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+
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+ ## Direct Use
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+
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+ Fill-Mask tasks.
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+
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+ ## Downstream Use
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+
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+ Morphological tagging and lemmatization, dependency parsing, named entity
42
+ recognition, and semantic parsing.
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+
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+
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+ # Bias, Risks, and Limitations
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+
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+ Significant research has explored bias and fairness issues with language models
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+ (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf)
49
+ and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
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+ Predictions generated by the model may include disturbing and harmful
51
+ stereotypes across protected classes; identity characteristics; and sensitive,
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+ social, and occupational groups.
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+
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+ ## Recommendations
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and
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+ limitations of the model. More information needed for further recommendations.
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+
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+
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+ # Training Details
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+
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+ ## Training Data
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+
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+ The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
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+ > We trained RobeCzech on a collection of the following publicly available texts:
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+ > - SYN v4, a large corpus of contemporary written Czech, 4,188M tokens;
67
+ > - Czes, a collection of Czech newspaper and magazine articles, 432M tokens;
68
+ > - documents with at least 400 tokens from the Czech part of the web corpus.W2C , tokenized with MorphoDiTa, 16M tokens;
69
+ > - plain texts extracted from Czech Wikipedia dump 20201020 using WikiEx-tractor, tokenized with MorphoDiTa, 123M tokens
70
+
71
+ > All these corpora contain whole documents, even if the SYN v4 is
72
+ > block-shuffled (blocks with at most 100 words respecting sentence boundaries
73
+ > are permuted in a document) and in total contain 4,917M tokens.
74
+
75
+ ## Training Procedure
76
+
77
+ ### Preprocessing
78
+
79
+ The texts are tokenized into subwords with a byte-level BPE (BBPE) tokenizer,
80
+ which was trained on the entire corpus and we limit its vocabulary size to
81
+ 52,000 items.
82
+
83
+ ### Speeds, Sizes, Times
84
+ The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
85
+ > The training batch size is 8,192 and each training batch consists of sentences
86
+ > sampled contiguously, even across document boundaries, such that the total
87
+ > length of each sample is at most 512 tokens (FULL-SENTENCES setting). We use
88
+ > Adam optimizer with β1 = 0.9 and β2 = 0.98 to minimize the masked
89
+ > language-modeling objective.
90
+
91
+ ### Software Used
92
+ The [Fairseq](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta)
93
+ implementation was used for training.
94
+
95
+
96
+ # Evaluation
97
+
98
+ ## Testing Data, Factors & Metrics
99
+
100
+ ### Testing Data
101
+ The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
102
+ > We evaluate RobeCzech in five NLP tasks, three of them leveraging frozen
103
+ > contextualized word embeddings, two approached with fine-tuning:
104
+ > - morphological analysis and lemmatization: frozen contextualized word embeddings,
105
+ > - dependency parsing: frozen contextualized word embeddings,
106
+ > - named entity recognition: frozen contextualized word embeddings,
107
+ > - semantic parsing: fine-tuned,
108
+ > - sentiment analysis: fine-tuned.
109
+
110
+ ## Results
111
+
112
+ | Model | Morphosynt PDT3.5 (POS) (LAS) | Morphosynt UD2.3 (XPOS) (LAS) | NER CNEC1.1 (nested) (flat) | Semant. PTG (Avg) (F1) |
113
+ |-----------|---------------------------------|--------------------------------|------------------------------|-------------------------|
114
+ | RobeCzech | 98.50 91.42 | 98.31 93.77 | 87.82 87.47 | 92.36 80.13 |
115
+
116
+
117
+ # Environmental Impact
118
+
119
+ - **Hardware Type:** 8 QUADRO P5000 GPU
120
+ - **Hours used:** 2190 (~3 months)
121
+
122
+
123
+ # Citation
124
+
125
+ ```
126
+ @InProceedings{10.1007/978-3-030-83527-9_17,
127
+ author={Straka, Milan and N{\'a}plava, Jakub and Strakov{\'a}, Jana and Samuel, David},
128
+ editor={Ek{\v{s}}tein, Kamil and P{\'a}rtl, Franti{\v{s}}ek and Konop{\'i}k, Miloslav},
129
+ title={{RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model}},
130
+ booktitle="Text, Speech, and Dialogue",
131
+ year="2021",
132
+ publisher="Springer International Publishing",
133
+ address="Cham",
134
+ pages="197--209",
135
+ isbn="978-3-030-83527-9"
136
+ }
137
+ ```
138
+
139
+
140
+ # How to Get Started with the Model
141
+
142
+ Use the code below to get started with the model.
143
+
144
+ <details>
145
+ <summary> Click to expand </summary>
146
+
147
+ ```python
148
+ from transformers import AutoTokenizer, AutoModelForMaskedLM
149
+
150
+ tokenizer = AutoTokenizer.from_pretrained("ufal/robeczech-base")
151
+
152
+ model = AutoModelForMaskedLM.from_pretrained("ufal/robeczech-base")
153
+ ```
154
+ </details>
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