Initial upload with a fixed tokenizer.

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

LICENSE

@@ -0,0 +1,438 @@
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README.md

@@ -1,3 +1,154 @@
+
 ---
+language: cs
 license: cc-by-nc-sa-4.0
+tags:
+- RobeCzech
+- Czech
+- RoBERTa
+- ÚFAL
 ---
+
+# Model Card for RobeCzech
+
+**If you are having issues with the tokenizer, please see https://huggingface.co/ufal/robeczech-base/discussions/4#64b8f6a7f1f8e6ea5860b314.**
+
+# Model Details
+
+## Model Description
+
+RobeCzech is a monolingual RoBERTa language representation model trained on Czech data.
+
+- **Developed by:** Institute of Formal and Applied Linguistics, Charles University, Prague (UFAL)
+- **Shared by:** Hugging Face and [LINDAT/CLARIAH-CZ](https://hdl.handle.net/11234/1-3691)
+- **Model type:** Fill-Mask
+- **Language(s) (NLP):** cs
+- **License:** cc-by-nc-sa-4.0
+- **Model Architecture:** RoBERTa
+- **Resources for more information:**
+  - [RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model](https://doi.org/10.1007/978-3-030-83527-9_17)
+    - [arXiv preprint is also available](https://arxiv.org/abs/2105.11314)
+
+
+# Uses
+
+## Direct Use
+
+Fill-Mask tasks.
+
+## Downstream Use
+
+Morphological tagging and lemmatization, dependency parsing, named entity
+recognition, and semantic parsing.
+
+
+# Bias, Risks, and Limitations
+
+Significant research has explored bias and fairness issues with language models
+(see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf)
+and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+Predictions generated by the model may include disturbing and harmful
+stereotypes across protected classes; identity characteristics; and sensitive,
+social, and occupational groups.
+
+## Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and
+limitations of the model. More information needed for further recommendations.
+
+
+# Training Details
+
+## Training Data
+
+The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
+> We trained RobeCzech on a collection of the following publicly available texts:
+> - SYN v4, a large corpus of contemporary written Czech, 4,188M tokens;
+> - Czes, a collection of Czech newspaper and magazine articles, 432M tokens;
+> - documents with at least 400 tokens from the Czech part of the web corpus.W2C , tokenized with MorphoDiTa, 16M tokens;
+> - plain texts extracted from Czech Wikipedia dump 20201020 using WikiEx-tractor, tokenized with MorphoDiTa, 123M tokens
+
+> All these corpora contain whole documents, even if the SYN v4 is
+> block-shuffled (blocks with at most 100 words respecting sentence boundaries
+> are permuted in a document) and in total contain 4,917M tokens.
+
+## Training Procedure
+
+### Preprocessing
+
+The texts are tokenized into subwords with a byte-level BPE (BBPE) tokenizer,
+which was trained on the entire corpus and we limit its vocabulary size to
+52,000 items.
+
+### Speeds, Sizes, Times
+The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
+> The training batch size is 8,192 and each training batch consists of sentences
+> sampled contiguously, even across document boundaries, such that the total
+> length of each sample is at most 512 tokens (FULL-SENTENCES setting). We use
+> Adam optimizer with β1 = 0.9 and β2 = 0.98 to minimize the masked
+> language-modeling objective.
+
+### Software Used
+The [Fairseq](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta)
+implementation was used for training.
+
+
+# Evaluation
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+The model creators note in the [associated paper](https://arxiv.org/pdf/2105.11314.pdf):
+> We evaluate RobeCzech in five NLP tasks, three of them leveraging frozen
+> contextualized word embeddings, two approached with fine-tuning:
+> - morphological analysis and lemmatization: frozen contextualized word embeddings,
+> - dependency parsing: frozen contextualized word embeddings,
+> - named entity recognition: frozen contextualized word embeddings,
+> - semantic parsing: fine-tuned,
+> - sentiment analysis: fine-tuned.
+
+## Results
+
+| Model     | Morphosynt PDT3.5   (POS) (LAS) | Morphosynt UD2.3  (XPOS) (LAS) | NER CNEC1.1  (nested) (flat) | Semant. PTG  (Avg) (F1) |
+|-----------|---------------------------------|--------------------------------|------------------------------|-------------------------|
+| RobeCzech | 98.50      91.42                | 98.31    93.77                 | 87.82	           87.47        | 92.36           80.13   |
+
+
+# Environmental Impact
+
+- **Hardware Type:** 8 QUADRO P5000 GPU
+- **Hours used:** 2190 (~3 months)
+
+
+# Citation
+
+```
+@InProceedings{10.1007/978-3-030-83527-9_17,
+  author={Straka, Milan and N{\'a}plava, Jakub and Strakov{\'a}, Jana and Samuel, David},
+  editor={Ek{\v{s}}tein, Kamil and P{\'a}rtl, Franti{\v{s}}ek and Konop{\'i}k, Miloslav},
+  title={{RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model}},
+  booktitle="Text, Speech, and Dialogue",
+  year="2021",
+  publisher="Springer International Publishing",
+  address="Cham",
+  pages="197--209",
+  isbn="978-3-030-83527-9"
+}
+```
+
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("ufal/robeczech-base")
+
+model = AutoModelForMaskedLM.from_pretrained("ufal/robeczech-base")
+ ```
+</details>

config.json

@@ -0,0 +1,22 @@
+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "type_vocab_size": 1,
+  "vocab_size": 51997
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1 @@
+{"bos_token": "[CLS]", "eos_token": "[SEP]", "sep_token": "[SEP]", "cls_token": "[CLS]", "unk_token": "[UNK]", "pad_token": "[PAD]", "mask_token": "[MASK]", "add_prefix_space": false}