Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,460 @@
+---
+base_model: manuel-couto-pintos/roberta_erisk
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:30288
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 'An inside look at Martha Stewart''s Stable  '
+  sentences:
+  - I feel like Im suffocating, Im empty and I feel like my heart is drowning. I try
+    everyday to be okay but its just so hard
+  - How did the Church and the YMCA organization feel about the YMCA being portrayed
+    in the song as a gay-friendly place, and the song becoming a gay anthem?
+  - 'An inside look at Martha Stewart''s Stable  '
+- source_sentence: 'Chemicals in marijuana may fight MRSA infections.  '
+  sentences:
+  - 'Our new opening sequence for The Walking Dead Please let me know what you think.
+    This was shot as a class project for an undergrad TV production class. The professor
+    really enjoyed it and called it better than the sequence on TV now (which I think
+    is shot better than ours, but much more boring).
+
+
+
+
+    EDIT: not sure if the link attached properly. It''s [here](http://vimeo.com/17275877) '
+  - 'Greek central bank warns of ''painful'' euro and EU exit  '
+  - 'Chemicals in marijuana may fight MRSA infections.  '
+- source_sentence: 'Pluggin Opiates.Ive been into opiates for years now, recently
+    I read from the FAQ about plugging and been trying it out for about a month or
+    so. Currently my DoC is Morphine. Im curious to know if there are many people
+    who use that ROA and Im curious in others experience with it. Ive never shot up,
+    only oral or plugging. For those who blissfully plug, what is your experience
+    like? Does your high last longer or is it less productive as other ROAs? I get
+    the best high from plugging and its essentially instant. Id appreciate some advice
+    or for you to share your experience with me? Thanks, stay high and mellow my opioid
+    lovers Side note: if you havent tried plugging I recommend it 100%, no shame,
+    just safe dosing.'
+  sentences:
+  - New Members IntroIf youre new to the community, introduce yourself!
+  - I found a Nat King Cole signed record at an antique mall for $12.50.
+  - 'Pluggin Opiates.Ive been into opiates for years now, recently I read from the
+    FAQ about plugging and been trying it out for about a month or so. Currently my
+    DoC is Morphine. Im curious to know if there are many people who use that ROA
+    and Im curious in others experience with it. Ive never shot up, only oral or plugging.
+    For those who blissfully plug, what is your experience like? Does your high last
+    longer or is it less productive as other ROAs? I get the best high from plugging
+    and its essentially instant. Id appreciate some advice or for you to share your
+    experience with me? Thanks, stay high and mellow my opioid lovers Side note: if
+    you havent tried plugging I recommend it 100%, no shame, just safe dosing.'
+- source_sentence: 'what can i do to be a likeable person? what do people look for
+    in friends? what determines our worth as a person? I realized that a lot of my
+    problems come from trying to impress people in order for them to like me and possibly
+    become friends.
+
+
+
+
+    but do people really look at all the things you''ve accomplished and all the things
+    you''ve done to determine if your worthy of being a friend? apparently that seems
+    to be my mindset, and that''s the reason I do things just to impress people
+
+
+
+
+    so what do people look for in others when determining whether they can be a good
+    friend or not. or another way to think of it, what determines our worth as a person? '
+  sentences:
+  - 'My humble reaction after I win an argument with my SO.  '
+  - When do i get to use the super troop potion[ask]
+  - 'what can i do to be a likeable person? what do people look for in friends? what
+    determines our worth as a person? I realized that a lot of my problems come from
+    trying to impress people in order for them to like me and possibly become friends.
+
+
+
+
+    but do people really look at all the things you''ve accomplished and all the things
+    you''ve done to determine if your worthy of being a friend? apparently that seems
+    to be my mindset, and that''s the reason I do things just to impress people
+
+
+
+
+    so what do people look for in others when determining whether they can be a good
+    friend or not. or another way to think of it, what determines our worth as a person? '
+- source_sentence: 'Goodnight, Texas - The Horse Accident (In Which A Girl Was All
+    But Killed)  '
+  sentences:
+  - Getting rid of ants - methods used in SL households?We have an ants problem in
+    our home. The small red ants, to be specific. It didnt always use to be this serious
+    but now its annoying that we cannot keep any unpacked food (bread, sugar, buns
+    etc.) outside the refrigerator for more than one day (with the lockdown weve stacked
+    up supplies enough for a week). What methods that you guys use to get rid or control
+    ants in your home? Its better if theres a way to completely get rid of them once
+    and for all but controlling methods are ok too. Better if theyre easy to find
+    in SL as most of the videos Ive found require separate chemicals and ingredients.
+  - 'Goodnight, Texas - The Horse Accident (In Which A Girl Was All But Killed)  '
+  - 'Creating Transit Gateway VPC attachmentWhen you go to create a TGW VPC attachment
+    it asks you at the bottom for subnet ID''s. AWS gives the below description for
+    this, but I''m having trouble understanding the significance of this option. Does
+    this mean I would have to create multiple TGW attachments per subnet per AZ for
+    a single UPC with multiple AZ-A subnets? Or as long as the subnets I chose share
+    a routing table with other subnets in the same AZ I am good?  For example I have
+    a VPC with: MGMT Subnet AZ-A using MGMT route table MGMT Subnet AZ-B using MGMT
+    route table Data Subnet in AZ-A using DATA route table Data Subnet in AZ-B using
+    DATA route table Would I need two TGW attachments, one for data subnets and one
+    for MGMT subnets?  " For **Subnet IDs**, select one subnet for each Availability
+    Zone to be used by the transit gateway to route traffic. You must select at least
+    one subnet. You can select only one subnet per Availability Zone. "'
+---
+
+# SentenceTransformer based on manuel-couto-pintos/roberta_erisk
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk) <!-- at revision 9aa8180ee595fe69a8d23c06dc5ee405f4f5d5ac -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("manuel-couto-pintos/roberta_erisk_simcse")
+# Run inference
+sentences = [
+    'Goodnight, Texas - The Horse Accident (In Which A Girl Was All But Killed)  ',
+    'Goodnight, Texas - The Horse Accident (In Which A Girl Was All But Killed)  ',
+    'Creating Transit Gateway VPC attachmentWhen you go to create a TGW VPC attachment it asks you at the bottom for subnet ID\'s. AWS gives the below description for this, but I\'m having trouble understanding the significance of this option. Does this mean I would have to create multiple TGW attachments per subnet per AZ for a single UPC with multiple AZ-A subnets? Or as long as the subnets I chose share a routing table with other subnets in the same AZ I am good?  For example I have a VPC with: MGMT Subnet AZ-A using MGMT route table MGMT Subnet AZ-B using MGMT route table Data Subnet in AZ-A using DATA route table Data Subnet in AZ-B using DATA route table Would I need two TGW attachments, one for data subnets and one for MGMT subnets?  " For **Subnet IDs**, select one subnet for each Availability Zone to be used by the transit gateway to route traffic. You must select at least one subnet. You can select only one subnet per Availability Zone. "',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 30,288 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 87.71 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 87.71 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Any teens want to talk about Rush? Being 15, it's hell on earth trying to find others around my age to talk about Rush or (thanks to my interests) anyone to talk about anything. Let me know if you're around 15 (preferably 13-18) and, I dunno, maybe we could make a Skype group/kik group/whatever.<br><br><br><br>Ghost of an edit: no personal info will be shared, don't worry. Felt I should clarify that. </code>                                                                                                                                         | <code>Any teens want to talk about Rush? Being 15, it's hell on earth trying to find others around my age to talk about Rush or (thanks to my interests) anyone to talk about anything. Let me know if you're around 15 (preferably 13-18) and, I dunno, maybe we could make a Skype group/kik group/whatever.<br><br><br><br>Ghost of an edit: no personal info will be shared, don't worry. Felt I should clarify that. </code>                                                                                                                                         |
+  | <code>Interesting video about racial inequality in the prison system.  </code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>Interesting video about racial inequality in the prison system.  </code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>[Intro] 30, F, dweebHi! My names Liz. Nice to meet you all. I love: - drawing - animation (went to animation school but dropped out) - video games (PC, Nintendo Switch, 3DS) - horror - cryptozoology - mystery science theater 3000 - documentaries - true crime - collecting steelbooks - amiibos It was my birthday this month and I just turned 30. I have chronic illness and depression but I try my best every day to stay positive. If you want I can do a little doodle of you if you want to share a picture! Looking forward to meeting everyone</code> | <code>[Intro] 30, F, dweebHi! My names Liz. Nice to meet you all. I love: - drawing - animation (went to animation school but dropped out) - video games (PC, Nintendo Switch, 3DS) - horror - cryptozoology - mystery science theater 3000 - documentaries - true crime - collecting steelbooks - amiibos It was my birthday this month and I just turned 30. I have chronic illness and depression but I try my best every day to stay positive. If you want I can do a little doodle of you if you want to share a picture! Looking forward to meeting everyone</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `num_train_epochs`: 5
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.1651 | 500   | 0.8492        |
+| 0.3301 | 1000  | 0.0013        |
+| 0.4952 | 1500  | 0.0007        |
+| 0.6603 | 2000  | 0.0007        |
+| 0.8254 | 2500  | 0.0003        |
+| 0.9904 | 3000  | 0.0           |
+| 1.1555 | 3500  | 0.0           |
+| 1.3206 | 4000  | 0.0           |
+| 1.4856 | 4500  | 0.0002        |
+| 1.6507 | 5000  | 0.0003        |
+| 1.8158 | 5500  | 0.0003        |
+| 1.9809 | 6000  | 0.0           |
+| 2.1459 | 6500  | 0.0           |
+| 2.3110 | 7000  | 0.0           |
+| 2.4761 | 7500  | 0.0           |
+| 2.6411 | 8000  | 0.0003        |
+| 2.8062 | 8500  | 0.0003        |
+| 2.9713 | 9000  | 0.0           |
+| 3.1363 | 9500  | 0.0           |
+| 3.3014 | 10000 | 0.0           |
+| 3.4665 | 10500 | 0.0002        |
+| 3.6316 | 11000 | 0.0003        |
+| 3.7966 | 11500 | 0.0003        |
+| 3.9617 | 12000 | 0.0           |
+| 4.1268 | 12500 | 0.0           |
+| 4.2918 | 13000 | 0.0           |
+| 4.4569 | 13500 | 0.0           |
+| 4.6220 | 14000 | 0.0003        |
+| 4.7871 | 14500 | 0.0003        |
+| 4.9521 | 15000 | 0.0           |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.0.1+cu117
+- Accelerate: 0.32.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "manuel-couto-pintos/roberta_erisk",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "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,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "RobertaTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.0.1+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44fc902cab35bec2eefc869e403decd1a8bd4a837542904de24a8d3e162abbb7
+size 498604904

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}