This model is a fine-tuned version of [ifis-zork/ZORK_AI_FAN_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_FAN_TEMP) on an unkown dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3


model name: ZORK_AI_FANTASY


model author: ifis-zork


task: text-generation


tags: pytorch, tensorboard, gpt2, text-generation, transformers, generated_from_trainer


tags:


- generated_from_trainer
model_index:
- name: ZORK_AI_FAN_TEMP
results:
  - task:
name: Causal Language Modeling
type: text-generation


<!-- 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. -->


# ZORK_AI_FAN_TEMP


This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


### Framework versions


model name: ZORK_AI_FAN_TEMP


model author: ifis-zork


task: text-generation


- generated_from_trainer
model_index:
- name: ZORK_AI_MODERN_A
results:
  - task:
name: Causal Language Modeling
type: text-generation


# ZORK_AI_MODERN_A


model name: ZORK_AI_MODERN_A


tags:


- generated_from_trainer
model_index:
- name: ZORK_AI_MODERN
results:
  - task:
name: Causal Language Modeling
type: text-generation


# ZORK_AI_MODERN


This model is a fine-tuned version of [ifis-zork/ZORK_AI_MODERN_A](https://huggingface.co/ifis-zork/ZORK_AI_MODERN_A) on an unkown dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


### Framework versions


model name: ZORK_AI_MODERN


model author: ifis-zork


task: text-generation


- generated_from_trainer
model_index:
- name: ZORK_AI_SCI_FI_TEMP
results:
  - task:
name: Causal Language Modeling
type: text-generation


# ZORK_AI_SCI_FI_TEMP


model name: ZORK_AI_SCI_FI_TEMP


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- glue
model_index:
- name: name
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc


# name


This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


model name: name


model author: ksmcg


task: text-classification


tags: pytorch, tensorboard, bert, text-classification, dataset:glue, transformers, license:apache-2.0, generated_from_trainer, infinity_compatible


# ProteinLM


model name: ProteinLM


model author: Yijia


tags:


kk


model name: mmm


model author: ntest


language:


- es
datasets:
- squad_es
widget:
- text: "¿Quién era el duque en la batalla de Hastings?"
context: "La dinastía normanda tuvo un gran impacto político, cultural y militar en la Europa medieval e incluso en el Cercano Oriente. Los normandos eran famosos por su espíritu marcial y, finalmente, por su piedad cristiana, convirtiéndose en exponentes de la ortodoxia católica en la que se asimilaron. Adoptaron la lengua galorromance de la tierra franca que establecieron, siendo su dialecto conocido como francés normando, normando o normando, una lengua literaria importante. El ducado de Normandía, que formaron por tratado con la corona francesa, fue un gran feudo de la Francia medieval, y bajo Ricardo I de Normandía se forjó en un principado cohesionado y formidable en la tenencia feudal. Los normandos se caracterizan tanto por su cultura, como por su singular arquitectura románica y sus tradiciones musicales, y por sus importantes logros e innovaciones militares. Aventureros normandos fundaron el Reino de Sicilia bajo Roger II después de conquistar el sur de Italia con los sarracenos y bizantinos, y una expedición en nombre de su duque, Guillermo el Conquistador, condujo a la conquista normanda de Inglaterra. La influencia cultural y militar normanda se extendió desde estos nuevos centros europeos a los estados cruzados del Cercano Oriente, donde su príncipe Bohemundo I fundó el Principado de Antioquía en el Levante mediterráneo, a Escocia y Gales en Gran Bretaña."


This is the [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) model ([source](https://github.com/PlanTL-SANIDAD/lm-spanish)) trained on the [squad_es v2.0.0](https://huggingface.co/datasets/squad_es) dataset ([source](https://github.com/ccasimiro88/TranslateAlignRetrieve)).


Current achievement: em=58.80, f1=67.40


Results:


```
{
    "epoch": 4.0,
    "eval_HasAns_exact": 48.51551956815115,
    "eval_HasAns_f1": 65.70745010262016,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 69.0893760539629,
    "eval_NoAns_f1": 69.0893760539629,
    "eval_NoAns_total": 5930,
    "eval_best_exact": 58.804182830156854,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 67.39869828034618,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 58.804182830156854,
    "eval_f1": 67.39869828034568,
    "eval_samples": 12211,
    "eval_total": 11858
}
```


Training script:


```
python -m torch.distributed.launch --nproc_per_node=3 ./run_qa.py \
    --model_name_or_path BSC-TeMU/roberta-base-bne \
    --dataset_name squad_es \
    --dataset_config_name v2.0.0 \
    --do_train \
    --do_eval \
    --learning_rate 3e-5 \
    --num_train_epochs 4 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./models/roberta-base-bne-squad-2.0-es/ \
    --per_device_eval_batch_size=8   \
    --per_device_train_batch_size=8   \
    --version_2_with_negative \
    --ddp_find_unused_parameters=False \
    --overwrite_output_dir \
```


model name: roberta-base-bne-squad-2.0-es


model author: jamarju


task: question-answering


tags: pytorch, roberta, question-answering, es, dataset:squad_es, transformers, autonlp_compatible, infinity_compatible


This is the [BSC-TeMU/roberta-large-bne](https://huggingface.co/BSC-TeMU/roberta-large-bne) model ([source](https://github.com/PlanTL-SANIDAD/lm-spanish)) trained on the [squad_es v2.0.0](https://huggingface.co/datasets/squad_es) dataset ([source](https://github.com/ccasimiro88/TranslateAlignRetrieve)).


Current achievement: em=60.21, f1=68.61


```
{
    "epoch": 4.0,
    "eval_HasAns_exact": 48.44804318488529,
    "eval_HasAns_f1": 65.24520506718169,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 71.97301854974705,
    "eval_NoAns_f1": 71.97301854974705,
    "eval_NoAns_total": 5930,
    "eval_best_exact": 60.22094788328555,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 68.6181122987237,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 60.2125147579693,
    "eval_f1": 68.60967917340695,
    "eval_samples": 12203,
    "eval_total": 11858
}
```


```
python -m torch.distributed.launch --nproc_per_node=3 ./run_qa.py \
    --model_name_or_path BSC-TeMU/roberta-large-bne \
    --dataset_name squad_es \
    --dataset_config_name v2.0.0 \
    --do_train \
    --do_eval \
    --learning_rate 3e-5 \
    --num_train_epochs 4 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir ./models/roberta-large-bne-finetuned-squad-es/ \
    --per_device_eval_batch_size=24   \
    --per_device_train_batch_size=12   \
    --version_2_with_negative \
    --ddp_find_unused_parameters=False \
```


model name: roberta-large-bne-squad-2.0-es


Wav2Vec2 Model


model name: Wav2Vec2


model author: Scrya


tags:


language:


- zh
tags:
- bert
- pytorch
- environment
- multi-class
- classification


中文环境文本分类模型，1.6M的数据集，在env-bert-chinese上进行fine-tuning。\
分为环境影响评价与控制、碳排放控制、水污染控制、大气污染控制、土壤污染控制、环境生态、固体废物、环境毒理与健康、环境微生物、环境政策与经济10类。


项目正在进行中，后续会陆续更新相关内容。\
清华大学环境学院课题组\
有相关需求、建议，联系bi.huaibin@foxmail.com


model name: env-bert-cls-chinese


model author: celtics1863


task: text-classification


tags: pytorch, bert, text-classification, zh, transformers, environment, multi-class, classification, infinity_compatible


language: zh
widget:


- text: "总[MASK]是水环境中的重要污染物。"
- text: "气[MASK]变化是重要的全球环境问题。"


- pretrain
- pytorch
- environment


环境领域的中文预训练Bert模型，在hlf/chinese-bert-wwm-ext的基础上进行训练，旨在学习到中文表达后进一步学习到环境领域的专业知识。
1.5G的预训练语料包括水环境、大气环境、土壤环境、气候变化、中文期刊、国家政策等内容。


model name: env-bert-chinese


task: fill-mask


tags: pytorch, bert, fill-mask, zh, transformers, pretrain, environment, autonlp_compatible, infinity_compatible


language: zh


widget:


- text: "美国退出《巴黎协定》"
- text: "污水处理厂中的功耗需要减少"


tags:


- pretrain
- pytorch
- environment
- classification
- topic classification


话题分类模型，使用某乎"环境"话题下所有子话题，过滤后得69类。\
top1 acc 60.7,\
top3 acc 81.6，\
可以用于中文环境文本挖掘的预处理步骤。


标签：
"生态环境","水污染", "野生动物保护", "太阳能", "环保经济", "污水处理", "绿色建筑", "水处理", "噪音污染", "温室效应", "净水设备",
"净水器", "自来水", "生活", "环境评估", "空气污染", "环境评价", "工业污染", "雾霾", "植树", "环保行业", "水处理工程", "沙漠治理",
"巴黎协定", "核能", "噪音", "环评工程师", "二氧化碳", "低碳", "自然环境", "沙尘暴", "环境工程", "秸秆焚烧", "PM 2.5", "太空垃圾",
"穹顶之下（纪录片）", "垃圾", "环境科学", "净水", "污水排放", "室内空气污染", "环境污染", "全球变暖", "邻居噪音", "土壤污染", "生物多样性",
"碳交易", "污染治理", "雾霾治理", "碳金融", "建筑节能", "风能及风力发电", "温室气体", "环境保护", "碳排放", "垃圾处理器", "气候变化", "化学污染",
"地球一小时", "环保组织", "物种多样性", "节能减排", "核污染", "环保督查", "垃圾处理", "垃圾分类", "重金属污染", "环境伦理学", "垃圾焚烧"


model name: env-bert-topic


model author: celtics1863


task: text-classification


tags: pytorch, bert, text-classification, zh, transformers, pretrain, environment, classification, topic classification, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep5_PAD_75-finetuned-ner
results: []


<!-- 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. -->


# BERT_NER_Ep5_PAD_75-finetuned-ner


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3504
- Precision: 0.6469
- Recall: 0.7246
- F1: 0.6835
- Accuracy: 0.9013


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 288  | 0.3695          | 0.5799    | 0.6200 | 0.5993 | 0.8792   |
| 0.4695        | 2.0   | 576  | 0.3443          | 0.5823    | 0.7252 | 0.6460 | 0.8862   |
| 0.4695        | 3.0   | 864  | 0.3189          | 0.6407    | 0.7030 | 0.6704 | 0.8978   |
| 0.2184        | 4.0   | 1152 | 0.3458          | 0.6383    | 0.7335 | 0.6826 | 0.8980   |
| 0.2184        | 5.0   | 1440 | 0.3504          | 0.6469    | 0.7246 | 0.6835 | 0.9013   |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: BERT_NER_Ep5_PAD_75-finetuned-ner


model author: suwani


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []


# distilbert-base-uncased-finetuned-ner


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.2787
- Precision: 0.6403
- Recall: 0.6929
- F1: 0.6655
- Accuracy: 0.9100


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 288  | 0.3360          | 0.5596    | 0.5992 | 0.5788 | 0.8956   |
| 0.4686        | 2.0   | 576  | 0.2901          | 0.6061    | 0.7231 | 0.6594 | 0.9063   |
| 0.4686        | 3.0   | 864  | 0.2787          | 0.6403    | 0.6929 | 0.6655 | 0.9100   |


### Framework versions


- Transformers 4.11.0
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-ner


model author: suwani


task: token-classification


tags: pytorch, tensorboard, distilbert, token-classification, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep6_PAD_50-finetuned-ner
results: []


<!-- 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. -->


# BERT_NER_Ep6_PAD_50-finetuned-ner


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3741
- Precision: 0.6510
- Recall: 0.7399
- F1: 0.6926
- Accuracy: 0.9020


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 288  | 0.3648          | 0.5949    | 0.5907 | 0.5928 | 0.8792   |
| 0.4815        | 2.0   | 576  | 0.3400          | 0.5860    | 0.7390 | 0.6536 | 0.8867   |
| 0.4815        | 3.0   | 864  | 0.3217          | 0.6404    | 0.7129 | 0.6747 | 0.8992   |
| 0.2206        | 4.0   | 1152 | 0.3430          | 0.6413    | 0.7321 | 0.6837 | 0.8995   |
| 0.2206        | 5.0   | 1440 | 0.3560          | 0.6464    | 0.7377 | 0.6890 | 0.9010   |
| 0.1487        | 6.0   | 1728 | 0.3741          | 0.6510    | 0.7399 | 0.6926 | 0.9020   |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: BERT_NER_Ep6_PAD_50-finetuned-ner


model author: suwani


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: try_connll-finetuned-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
    - name: Precision
type: precision
value: 0.9283102493074792
    - name: Recall
type: recall
value: 0.9372413021590782
    - name: F1
type: f1
value: 0.9327543976842575
    - name: Accuracy
type: accuracy
value: 0.9840818466328817


# try_connll-finetuned-ner


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0596
- Precision: 0.9283
- Recall: 0.9372
- F1: 0.9328
- Accuracy: 0.9841


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2383        | 1.0   | 878  | 0.0691          | 0.9139    | 0.9239 | 0.9189 | 0.9810   |
| 0.0497        | 2.0   | 1756 | 0.0607          | 0.9200    | 0.9343 | 0.9271 | 0.9833   |
| 0.0303        | 3.0   | 2634 | 0.0596          | 0.9283    | 0.9372 | 0.9328 | 0.9841   |


### Framework versions


- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: try_connll-finetuned-ner


model author: suwani


task: token-classification


tags: pytorch, tensorboard, distilbert, token-classification, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep5_PAD_50-finetuned-ner
results: []


<!-- 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. -->


# BERT_NER_Ep5_PAD_50-finetuned-ner


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3893
- Precision: 0.6540
- Recall: 0.7348
- F1: 0.6920
- Accuracy: 0.9006


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 288  | 0.3705          | 0.5852    | 0.6215 | 0.6028 | 0.8793   |
| 0.4885        | 2.0   | 576  | 0.3351          | 0.5925    | 0.7317 | 0.6548 | 0.8865   |
| 0.4885        | 3.0   | 864  | 0.3196          | 0.6471    | 0.7138 | 0.6788 | 0.8994   |
| 0.2172        | 4.0   | 1152 | 0.3368          | 0.6454    | 0.7323 | 0.6861 | 0.8992   |
| 0.2172        | 5.0   | 1440 | 0.3491          | 0.6507    | 0.7312 | 0.6886 | 0.9008   |
| 0.1459        | 6.0   | 1728 | 0.3833          | 0.6715    | 0.7018 | 0.6863 | 0.9013   |
| 0.1045        | 7.0   | 2016 | 0.3893          | 0.6540    | 0.7348 | 0.6920 | 0.9006   |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3


model name: BERT_NER_Ep5_PAD_50-finetuned-ner


model author: suwani


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep5-finetuned-ner
results: []


# BERT_NER_Ep5-finetuned-ner


- Loss: 0.3553
- Precision: 0.6526
- Recall: 0.7248
- F1: 0.6868
- Accuracy: 0.9004


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 288  | 0.3675          | 0.5906    | 0.5854 | 0.5880 | 0.8802   |
| 0.4803        | 2.0   | 576  | 0.3456          | 0.5863    | 0.7371 | 0.6531 | 0.8864   |
| 0.4803        | 3.0   | 864  | 0.3273          | 0.6478    | 0.7091 | 0.6771 | 0.8987   |
| 0.2233        | 4.0   | 1152 | 0.3441          | 0.6539    | 0.7226 | 0.6865 | 0.9001   |
| 0.2233        | 5.0   | 1440 | 0.3553          | 0.6526    | 0.7248 | 0.6868 | 0.9004   |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: BERT_NER_Ep5-finetuned-ner


model author: suwani


task: token-classification


tags:


- conversational


# Cartman DialoGPT Model


model name: DialoGPT-mid-cartman


model author: Soapsy


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


- conversational
license: mit


## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript)


```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
model = AutoModelForCausalLM.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
# Let's chat for 5 lines
for step in range(5):
	# encode the new user input, add the eos_token and return a tensor in Pytorch
	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
	# append the new user input tokens to the chat history
	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
	# generated a response while limiting the total chat history to 1000 tokens,
    chat_history_ids = model.generate(
        bot_input_ids, max_length=200,
        pad_token_id=tokenizer.eos_token_id,
        no_repeat_ngram_size=3,
        do_sample=True,
        top_k=100,
        top_p=0.7,
        temperature = 0.8
    )
    	# pretty print last ouput tokens from bot
    print("TBBT Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```


model name: DialoGPT-small-Big-Bang-Theory-Series-Transcripts


model author: vijayv500


tags: pytorch, gpt2, text-generation, transformers, conversational, license:mit


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []


<!-- 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. -->


# distilbert-base-uncased-finetuned-squad


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:


- Loss: 1.1610


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2137        | 1.0   | 5533  | 1.1625          |
| 0.9496        | 2.0   | 11066 | 1.1263          |
| 0.7591        | 3.0   | 16599 | 1.1610          |


### Framework versions


- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-squad


model author: vitusya


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


model name: Twitter


model author: Legendarysoren


tags:


# Model name


## Model description


This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model.


## Intended uses & limitations


The model is trained to generate reading comprehension-style questions with answers extracted from a text. The model performs best with full sentence answers, but can also be used with single word or short phrase answers.


#### How to use


The model takes concatenated answers and context as an input sequence, and will generate a full question sentence as an output sequence. The max sequence length is 512 tokens. Inputs should be organised into the following format:


```
answer_token <answer-phrase> context_token <context-from-text>
```


The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.


For best results, a large number of questions can be generated, and then filtered using [iarfmoose/bert-base-cased-qa-evaluator](https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator).


For examples, please see https://github.com/iarfmoose/question_generator.


#### Limitations and bias


The model is limited to generating questions in the same style as those found in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/). The generated questions can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context and answer do not match, the generated question is likely to be incoherent.


## Training data


The model was fine-tuned on a dataset made up of several well-known QA datasets ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/)). The datasets were restructured by concatenating the answer and context fields into the previously-mentioned format. The question field from the datasets was used as the target during training. The full training set was roughly 200,000 examples.


## Training procedure


The model was trained for 20 epochs over the training set with a learning rate of 1e-3. The batch size was only 4 due to GPU memory limitations when training on Google Colab.


model name: t5-base-question-generator


model author: iarfmoose


task: text2text-generation


tags: pytorch, tf, jax, t5, text2text-generation, transformers, autonlp_compatible


## language: bg


# RoBERTa-small-bulgarian


The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a smaller version of [RoBERTa-base-bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) with only 6 hidden layers, but similar performance.


## Intended uses


This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.


## Limitations and bias


The training data is unfiltered text from the internet and may contain all sorts of biases.


This model was trained on the following data:


- [bg_dedup from OSCAR](https://oscar-corpus.com/)
- [Newscrawl 1 million sentences 2017 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
- [Wikipedia 1 million sentences 2016 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)


The model was pretrained using a masked language-modeling objective with dynamic masking as described [here](https://huggingface.co/roberta-base#preprocessing)


It was trained for 160k steps. The batch size was limited to 8 due to GPU memory limitations.


model name: roberta-small-bulgarian


task: fill-mask


tags: pytorch, tf, jax, roberta, fill-mask, bg, arxiv:1907.11692, transformers, autonlp_compatible, infinity_compatible


# RoBERTa-base-bulgarian


The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a version of [RoBERTa-base](https://huggingface.co/roberta-base) pretrained on Bulgarian text.


## Training data


## Training procedure


It was trained for 200k steps. The batch size was limited to 8 due to GPU memory limitations.


model name: roberta-base-bulgarian


model author: iarfmoose


## language: bg


# RoBERTa-small-bulgarian-POS


The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-small-Bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) fine-tuned for part-of-speech tagging.


## Intended uses


The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encoding, each word in the text may be split into more than one token. When predicting POS-tags, the last token from each word can be used. Using the last token was found to slightly outperform predictions based on the first token.


An example of this can be found [here](https://github.com/iarfmoose/bulgarian-nlp/blob/master/models/postagger.py).


## Limitations and bias


The pretraining data is unfiltered text from the internet and may contain all sorts of biases.


In addition to the pretraining data used in [RoBERTa-base-Bulgarian]([RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian)), the model was trained on the UPOS tags from (UD_Bulgarian-BTB)[https://github.com/UniversalDependencies/UD_Bulgarian-BTB].


The model was trained for 5 epochs over the training set. The loss was calculated based on label predictions for the last POS-tag for each word. The model achieves 98% on the test set.


model name: roberta-small-bulgarian-pos


task: token-classification


tags: pytorch, tf, jax, roberta, token-classification, bg, arxiv:1907.11692, transformers, autonlp_compatible, infinity_compatible


language: ky
datasets:


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Kyrgyz by Adam Montgomerie
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ky
type: common_voice
args: ky
metrics:
    - name: Test WER
type: wer
value: 34.71


# Wav2Vec2-Large-XLSR-53-Kyrgyz


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "ky", split="test[:2%]").

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\\\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Kyrgyz test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "ky", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\–\\\\\\\\—\\\\\\\\¬\\\\\\\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result**: 34.71 %


## Training


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Kyrgyz/XLSR_Kyrgyz.ipynb)


A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Kyrgyz/wav2vec2_ky_eval.ipynb)


model name: wav2vec2-large-xlsr-kyrgyz


model author: iarfmoose


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, ky, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


# BERT-base-cased-qa-evaluator


This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [BERT-base-cased](https://huggingface.co/bert-base-cased) with a sequence classification head.


## Intended uses


The QA evaluator was originally designed to be used with the [t5-base-question-generator](https://huggingface.co/iarfmoose/t5-base-question-generator) for evaluating the quality of generated questions.


The input for the QA evaluator follows the format for `BertForSequenceClassification`, but using the question and answer as the two sequences. Inputs should take the following format:


```
[CLS] <question> [SEP] <answer [SEP]
```


## Limitations and bias


The model is trained to evaluate if a question and answer are semantically related, but cannot determine whether an answer is actually true/correct or not.


## Training data


The training data was made up of question-answer pairs from the following datasets:


- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
- [RACE](http://www.cs.cmu.edu/~glai1/data/race/)
- [CoQA](https://stanfordnlp.github.io/coqa/)
- [MSMARCO](https://microsoft.github.io/msmarco/)


## Training procedure


The question and answer were concatenated 50% of the time. In the other 50% of the time a corruption operation was performed (either swapping the answer for an unrelated answer, or by copying part of the question into the answer). The model was then trained to predict whether the input sequence represented one of the original QA pairs or a corrupted input.


model name: bert-base-cased-qa-evaluator


model author: iarfmoose


task: text-classification


tags: pytorch, tf, jax, bert, text-classification, transformers, infinity_compatible


## language: bg


# RoBERTa-base-bulgarian-POS


The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian) fine-tuned for part-of-speech tagging.


## Intended uses


The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encoding, each word in the text may be split into more than one token. When predicting POS-tags, the last token from each word can be used. Using the last token was found to slightly outperform predictions based on the first token.


An example of this can be found [here](https://github.com/iarfmoose/bulgarian-nlp/blob/master/models/postagger.py).


## Limitations and bias


The pretraining data is unfiltered text from the internet and may contain all sorts of biases.


## Training data


In addition to the pretraining data used in [RoBERTa-base-Bulgarian]([RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian)), the model was trained on the UPOS tags from [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB).


The model was trained for 5 epochs over the training set. The loss was calculated based on label predictions for the last POS-tag for each word. The model achieves 97% on the test set.


model name: roberta-base-bulgarian-pos


task: token-classification


tags: pytorch, tf, jax, roberta, token-classification, bg, arxiv:1907.11692, transformers, autonlp_compatible, infinity_compatible


language: hsb
datasets:


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Sorbian by Adam Montgomerie
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice hsb
type: common_voice
args: hsb\
metrics:
    - name: Test WER
type: wer
value: 41.74


# Wav2Vec2-Large-XLSR-53-Sorbian


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Sorbian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "hsb", split="test[:2%]").

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Sorbian test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "hsb", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-sorbian")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�\\\\\\\\\\\\\\\\–\\\\\\\\\\\\\\\\—\\\\\\\\\\\\\\\\¬\\\\\\\\\\\\\\\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result**: 41.74 %


## Training


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Sorbian/XLSR_Sorbian.ipynb)


A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Sorbian/wav2vec2_hsb_eval.ipynb)


model name: wav2vec2-large-xlsr-sorbian


model author: iarfmoose


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, hsb, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


language: fy-NL
datasets:


- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Frisian by Adam Montgomerie
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fy-NL
type: common_voice
args: fy-NL\
metrics:
    - name: Test WER
type: wer
value: 21.72


# Wav2Vec2-Large-XLSR-53-Frisian


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "fy-NL", split="test[:2%]").

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Frisian test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "fy-NL", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-frisian")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\–\\—\\¬\\⅛]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result**: 21.72 %


## Training


The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Frisian/XLSR_Frisian.ipynb)


A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Frisian/wav2vec2_fyNL_eval.ipynb)


model name: wav2vec2-large-xlsr-frisian


model author: iarfmoose


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, fy-NL, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


Note that model type is Camembert.


model name: wolfbbsRoBERTa-small


model author: itsunoda


task: fill-mask


tags: pytorch, tf, camembert, fill-mask, transformers, autonlp_compatible


hello


model name: example-based-ner-bert


model author: argv947059


task: feature-extraction


tags: pytorch, jax, bert, feature-extraction, transformers, infinity_compatible


This a BERT-based QA model finetuned to answer causal questions. The original model this is based on can be found [here](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2).  Analysis of this model is associated with the work found at the following [repo](https://github.com/kstats/CausalQG).


model name: causal_qa


model author: manav


task: question-answering


tags: pytorch, jax, bert, question-answering, transformers, autonlp_compatible, infinity_compatible


tags:


- conversational


## Model description


Finetuned version of DialogPT-medium released. Finetuned on data scraped from the r/Berkeley subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when this model until further analysis of its biases can be performed.


model name: dialogpt-medium-berkeley-reddit


task: conversational


tags: pytorch, jax, gpt2, text-generation, transformers, conversational


Finetuned version of DialogPT-large released. Finetuned on data scraped from the r/Kanye subreddit. The data wasn't thoroughly vetted so the model may display biases that I am unaware of, so tread with caution when using this model until further analysis of its biases can be performed.


model name: dialogpt-large-kanye-reddit


license: cc-by-nc-sa-4.0
tags:


- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-funsd-test
results: []


<!-- 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. -->


# layoutlmv2-finetuned-funsd-test


This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000


### Training results


### Framework versions


- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.0
- Tokenizers 0.11.0


model name: layoutlmv2-finetuned-funsd-test


model author: dhanesh123in


task: token-classification


tags: pytorch, tensorboard, layoutlmv2, token-classification, transformers, license:cc-by-nc-sa-4.0, generated_from_trainer, model-index, autonlp_compatible


## FinBERT


Code for importing and using this model is available [here](https://github.com/ipuneetrathore/BERT_models)


model name: bert-base-cased-finetuned-finBERT


model author: ipuneetrathore


task: text-classification


tags: pytorch, jax, bert, text-classification, transformers, infinity_compatible


language:


- en
thumbnail:
tags:
- conversational
license: apache-2.0
datasets:
- rick_and_morty
metrics:
- perplexity


## Model description


Fine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty


model name: movie-chat


model author: sergunow


task: conversational


tags: pytorch, blenderbot, text2text-generation, en, dataset:rick_and_morty, transformers, conversational, license:apache-2.0, autonlp_compatible


pipeline_tag: sentence-similarity
language: fr
tags:


- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- fr
datasets:
- xnli
- stsb_multi_mt


# hugorosen/flaubert_base_uncased-xnli-sts


This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


<!--- Describe your model here -->


## Usage (Sentence-Transformers)


Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:


```
pip install -U sentence-transformers
```


Then you can use the model like this:


```python
from sentence_transformers import SentenceTransformer
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]

model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts')
embeddings = model.encode(sentences)
print(embeddings)
```


## Usage (HuggingFace Transformers)


Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.


```python
from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```


## Evaluation Results


<!--- Describe how your model was evaluated -->


This model scores 76.9% on STS test (french)


## Training


### Pre-training


We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure.


### Fine-tuning


we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french).


Parameters of the fit()-Method:


```
{
    "epochs": 4,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 144,
    "weight_decay": 0.01
}
```


## Full Model Architecture


```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: FlaubertModel
  (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})
)
```


## Citing & Authors


Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.


Based on FlauBERT:


```
@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```


model name: flaubert_base_uncased-xnli-sts


model author: hugorosen


task: sentence-similarity


tags: pytorch, flaubert, feature-extraction, fr, dataset:xnli, dataset:stsb_multi_mt, sentence-transformers, sentence-similarity, transformers


tags:


- conversational


# Atakan DialoGPT Model


model name: DialoGPT-medium-atakan


model author: CopymySkill


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


### Finetuned on annual report sentence pair


This marianMT has been further finetuned on annual report sentence pairs


## Test out at huggingface spaces!


## Sample colab notebook


## How to use


```python
!pip install transformers
!pip install sentencepiece

from transformers import MarianMTModel, MarianTokenizer

tokenizer = MarianTokenizer.from_pretrained("wolfrage89/annual_report_translation_id_en")
model = MarianMTModel.from_pretrained("wolfrage89/annual_report_translation_id_en")

#tokenizing bahasa sentence
bahasa_sentence = "Interpretasi ini merupakan interpretasi atas PSAK 46: Pajak Penghasilan yang bertujuan untuk mengklarifikasi dan memberikan panduan dalam merefleksikan ketidakpastian perlakuan pajak penghasilan dalam laporan keuangan."
tokenized_bahasa_sentence = tokenizer([bahasa_sentence], return_tensors='pt', max_length=104, truncation=True)

#feeding tokenized sentence into model, the max_legnth have been set to 104 as the model was trained mostly on sentences with this length
translated_tokens = model.generate(**tokenized_bahasa_sentence, max_length=104)[0]

## decoding the tokens to get english sentence
english_sentence = tokenizer.decode(translated_tokens, skip_special_tokens=True)

print(english_sentence)
# This interpretation is an interpretation of PSAK 46: Income Tax that aims to clarify and provide guidance in reflecting the uncertainty of income tax treatments in the financial statements.

```


### opus-mt-id-en (original model)


* source languages: id
* target languages: en
* OPUS readme: [id-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-en/README.md)


model name: annual_report_translation_id_en


model author: wolfrage89


task: text2text-generation


tags: pytorch, marian, text2text-generation, transformers, autonlp_compatible


## Roberta based NER


This model will take in a new article label 3 entities [ORGS, SEGNUM, NUM]. This model is train on reuters news articles


## Try out on huggingface Spaces


## colab sample notebook


```python

from transformers import pipeline

# Minimum code
sentence = """Exxon Mobil Corporation is engaged in energy business. The Company is engaged in the exploration, production, trade, transportation and sale of crude oil and natural gas, and the manufacture, transportation and sale of crude oil, natural gas, petroleum products, petrochemicals and a range of specialty products. The Company's segments include Upstream, Downstream, Chemical, and Corporate and Financing. The Upstream segment operates to explore for and produce crude oil and natural gas. The Downstream manufactures, trades and sells petroleum products. The refining and supply operations consists of a global network of manufacturing plants, transportation systems, and distribution centers that provide a range of fuels, lubricants and other products and feedstocks to its customers around the world. The Chemical segment manufactures and sells petrochemicals. The Chemical business supplies olefins, polyolefins, aromatics, and a variety of other petrochemicals."""

model = pipeline('ner', "wolfrage89/company_segment_ner")
model_output = model(sentence)

print(model_ouput)
# [{'entity': 'B-ORG', 'score': 0.99996805, 'index': 1, 'word': 'Ex', 'start': 0, 'end': 2}, {'entity': 'I-ORG', 'score': 0.99971646, 'index': 2, 'word': 'xon', 'start': 2, 'end': 5}, ....]

# Sample helper function if you want to use
def ner_prediction(model, sentence):
    entity_map = {
        "B-ORG":"ORG",
        "B-SEG":"SEG",
        "B-SEGNUM":"SEGNUM"
    }
    results = []
    model_output = model(sentence)

    accumulate = ""
    current_class = None
    start = 0
    end = 0
    for item in model_output:
        if item['entity'].startswith("B"):
            if len(accumulate) >0:
                results.append((current_class, accumulate, start, end))
            accumulate = item['word'].lstrip("Ġ")
            current_class = entity_map[item['entity']]
            start=item['start']
            end = item['end']

        else:
            if item['word'].startswith("Ġ"):
                accumulate+=" "+item['word'].lstrip("Ġ")

            else:
                accumulate+=item['word']
            end = item['end']

    # clear last cache
    if len(accumulate)>0:
         results.append((current_class, accumulate, start, end))

    return results

```


model name: company_segment_ner


task: token-classification


tags: pytorch, roberta, token-classification, transformers, autonlp_compatible, infinity_compatible


# dummy file!


model name: dummy


model author: j961224


tags:


- conversational


# Peppa Pig DialogGPT-small Model


model name: DialogGPT-small-peppa-pig


model author: umr55766


task: conversational


- spacy
- token-classification
language:
- pt
license: cc-by-sa-4.0
model-index:
- name: pt_udv25_portuguesebosque_trf
results:
  - task:
name: TAG
type: token-classification
metrics:
    - name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9809207592
  - task:
name: POS
type: token-classification
metrics:
    - name: POS (UPOS) Accuracy
type: accuracy
value: 0.9814163239
  - task:
name: MORPH
type: token-classification
metrics:
    - name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9733881758
  - task:
name: LEMMA
type: token-classification
metrics:
    - name: Lemma Accuracy
type: accuracy
value: 0.980030722
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.938526243
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Attachment Score (LAS)
type: f_score
value: 0.91190874
  - task:
name: SENTS
type: token-classification
metrics:
    - name: Sentences F-Score
type: f_score
value: 0.9582222222


UD v2.5 benchmarking pipeline for UD_Portuguese-Bosque


| Feature | Description |
| --- | --- |
| **Name** | `pt_udv25_portuguesebosque_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |


### Label Scheme


<details>


<summary>View label scheme (2079 labels for 6 components)</summary>