---
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Bónus feðgarnir Jóhannes Jónsson og Jón Ásgeir Jóhannesson opnuðu fyrstu Bónusbúðina í 400 fermetra húsnæði við Skútuvog laugardaginn 8. apríl 1989
model-index:
- name: XLMR-ENIS-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mim_gold_ner
      type: mim_gold_ner
      args: mim-gold-ner
    metrics:
    - name: Precision
      type: precision
      value: 0.861851332398317
    - name: Recall
      type: recall
      value: 0.8384309266628767
    - name: F1
      type: f1
      value: 0.849979828251974
    - name: Accuracy
      type: accuracy
      value: 0.9830620929487668
---

<!-- 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. -->

# XLMR-ENIS-finetuned-ner

This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0938
- Precision: 0.8619
- Recall: 0.8384
- F1: 0.8500
- Accuracy: 0.9831

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0574        | 1.0   | 2904 | 0.0983          | 0.8374    | 0.8061 | 0.8215 | 0.9795   |
| 0.0321        | 2.0   | 5808 | 0.0991          | 0.8525    | 0.8235 | 0.8378 | 0.9811   |
| 0.0179        | 3.0   | 8712 | 0.0938          | 0.8619    | 0.8384 | 0.8500 | 0.9831   |

### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


# SpERT
SpERT is the Relation Extraction model [(SpERT)Span-based Entity and Relation Transformer](https://github.com/lavis-nlp/spert).This is the model trained with CoNLL04 Dataset.

## Use

## References
```
Markus Eberts, Adrian Ulges. Span-based Joint Entity and Relation Extraction with Transformer Pre-training. 24th European Conference on Artificial Intelligence, 2020.
```


---
tags:
- conversational
---
# Monika Discord Chatbot


---
tags:
- conversational
---

# MoniKA unstable


---
tags:
- conversational
---
# Buro discord bot


---
tags:
- conversational
---
# Rin chatbot


---
language:
- en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
metrics:
- rouge
---

# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.

# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop46")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop46")
```

# Example Use
```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```

# Example output
```
[Score: -0.153]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the Red Planet.
[Score: -0.154]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the planet.
[Score: -0.154]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls have been spotted elsewhere on the Red Planet.
[Score: -0.195]  These tracks have been spotted elsewhere on Mars. If a rockfalls on Mars has been spotted elsewhere on the red planet. Scientists have spotted thousands of tracks on Mars. A rockfalls on Mars have been spotted elsewhere on the Red Planet. A rockfalls have been spotted everywhere on the red planet.
```

# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop


---
language:
- en
tags:
- simplification
license: apache-2.0
datasets:
- cnn_dailymail
widget:
- text: "A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>"
  example_title: "Example 1"
---

# Try out in the Hosted inference API
In the right panel, you can try the model (although it only handles a short sequence length).
Feel free to try Example 1, and modify it to inspect model ability.

# Model Loading

The model can be loaded in the following way:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("philippelaban/keep_it_simple")
kis_model = AutoModelForCausalLM.from_pretrained("philippelaban/keep_it_simple")
```

# Example use

And then used by first inputting a paragraph for simplification, followed by a `bos_token` to indicate to the model to start simplifying.
Imagine we want to simplify the following paragraph:
```
A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space
by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6.
The extremely high precision required to carry out the mission thrilled many in Japan,
who said they took pride in its success.
```

The following code can be run:
```
paragraph = """A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6. The extremely high precision required to carry out the mission thrilled many in Japan, who said they took pride in its success."""

start_id = tokenizer.bos_token_id
tokenized_paragraph = [(tokenizer.encode(text=paragraph) + [start_id])]
input_ids = torch.LongTensor(tokenized_paragraph)

output_ids = kis_model.generate(input_ids, max_length=150, num_beams=4, do_sample=True, num_return_sequences=8)
output_ids = output_ids[:, input_ids.shape[1]:]
output = tokenizer.batch_decode(output_ids)
output = [o.replace(tokenizer.eos_token, "") for o in output]

for o in output:
    print("----")
    print(o)
```

# Example output

When run, an output similar to the following should be obtained:

A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise, said many in Japan, and they took pride in its success.

A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise and well thought-out, said many in Japan, who took pride in the mission.

A small capsule containing soil samples that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was designed to test the performance of the country's space fleet, which many said took pride in its success.

A small capsule containing soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 probe was followed by a landing on the Outback. The precise timing of the mission thrilled many in Japan, who said they took pride in its success.

# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo:
https://github.com/tingofurro/keep_it_simple


---
language:
- en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
---

# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.

# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop10")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop10")
```

# Example Use
```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```

# Example output
```
[Score: -0.084]  Here's what you need to know about rockfalls
[Score: -0.087]  Here's what you need to know about these tracks
[Score: -0.091]  Here's what we know so far about these tracks
[Score: -0.101]  Here's what you need to know about rockfall
```

# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop


---
language:
- en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
---
# Try out in the Hosted inference API

In the right panel, you can try to the model (although it only handles a short sequence length).
Enter the document you want to summarize in the panel on the right.

# Model Loading
The model (based on a GPT2 base architecture) can be loaded in the following way:
```
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop46")
tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop46")
```

# Example Use
```
document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"

tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
input_shape = tokenized_document.shape
outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
candidate_scores = outputs.sequences_scores.tolist()

for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
    summary = tokenizer.decode(candidate_tokens)
    print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
```

# Example output
```
[Score: -0.113]  These tracks have been spotted elsewhere in the solar system, including on the red planet, and no one is there to see it, does it leave a trace? Yes, and
[Score: -0.119]  Now researchers have spotted thousands of tracks on the red planet created by tumbling boulders in Mars, and no one is there to see it, does it leave a trace?
[Score: -0.214]  Here are answers to those questions posed by scientists investigating the tracks discovered by scientists examining the tracks discovered by scientists exploring the tracks discovered by scientists exploring the tracks discovered by scientists exploring the
[Score: -0.388]  These are the kinds of questions swirling around whether these tracks exist on Mars, and whether they should be noticed sooner rather than later. Here are some answers: -- The tracks detected
```

# Github repo

You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop


---
tags:
- conversational
---

#GPT-2 model trained on Dana Scully's dialog.


---
tags:
- conversational
---

GPT-2 chatbot - talk to Fox Mulder


---
tags:
- conversational
---

GPT-2 chatbot - talk to Ray Smuckles


---
tags:
- conversational
---

GPT-2 chatbot - talk to Sonic


---
tags:
- conversational
---

GPT-2 model trained on Phil from Eastenders


---
tags:
- generated_from_trainer
model-index:
- name: translation-en-pt-t5-Duolingo-Subtitles
  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. -->

# translation-en-pt-t5-Duolingo-Subtitles

This model is a fine-tuned version of [unicamp-dl/translation-en-pt-t5](https://huggingface.co/unicamp-dl/translation-en-pt-t5) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7469
- eval_bleu: 39.9403
- eval_gen_len: 8.98
- eval_runtime: 997.6641
- eval_samples_per_second: 150.351
- eval_steps_per_second: 4.699
- epoch: 0.49
- step: 56000

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-zh-de-tuned-Tatoeba-small
  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. -->

# opus-mt-zh-de-tuned-Tatoeba-small

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md.
It achieves the following results on the evaluation set:
- Loss: 2.2703
- Bleu: 16.504
- Gen Len: 16.6531

## Model description

More information needed

## Intended uses & limitations

Prefix used during fine-tuning: "将中文翻译成德语". This prefix is also recommended in prediction.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 2.7229        | 0.24  | 16000  | 2.5605          | 14.1956 | 16.2206 |
| 2.5988        | 0.49  | 32000  | 2.4447          | 14.8619 | 16.2726 |
| 2.515         | 0.73  | 48000  | 2.3817          | 15.3212 | 16.2823 |
| 2.4683        | 0.97  | 64000  | 2.3367          | 15.9043 | 16.7138 |
| 2.3873        | 1.22  | 80000  | 2.3115          | 16.1037 | 16.6369 |
| 2.3792        | 1.46  | 96000  | 2.2919          | 16.2957 | 16.6304 |
| 2.3626        | 1.7   | 112000 | 2.2790          | 16.2995 | 16.6235 |
| 2.3353        | 1.95  | 128000 | 2.2703          | 16.504  | 16.6531 |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles
  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. -->

# translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles

This model was trained from scratch on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo-Subtitles
  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. -->

# translation-en-pt-t5-finetuned-Duolingo-Subtitles

This model is a fine-tuned version of [ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles](https://huggingface.co/ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0932
- eval_bleu: 28.4269
- eval_gen_len: 8.816
- eval_runtime: 1404.5946
- eval_samples_per_second: 106.792
- eval_steps_per_second: 3.338
- epoch: 0.52
- step: 28000

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translation-en-pt-t5-finetuned-Duolingo
  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. -->

# translation-en-pt-t5-finetuned-Duolingo

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7362
- Bleu: 39.4725
- Gen Len: 9.002

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.5429        | 0.24  | 9000  | 0.7461          | 39.4744 | 9.0     |
| 0.5302        | 0.48  | 18000 | 0.7431          | 39.7559 | 8.97    |
| 0.5309        | 0.72  | 27000 | 0.7388          | 39.6751 | 8.998   |
| 0.5336        | 0.96  | 36000 | 0.7362          | 39.4725 | 9.002   |

### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy

model-index:
- name: shirt_identifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6875
---

# shirt_identifier

Autogenerated by HuggingPics🤗🖼

Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).

Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).

## Example Images

#### Big Check shirt

![Big Check shirt](images/Big_Check_shirt.jpg)

#### Formal Shirt

![Formal Shirt](images/Formal_Shirt.jpg)

#### casual shirt

![casual shirt](images/casual_shirt.jpg)

#### denim shirt

![denim shirt](images/denim_shirt.jpg)


---
tags:
- conversational
---

# Harry Botter Model


---
language: no
thumbnail: https://i.imgur.com/QqSEC5I.png
---

# Norwegian Electra
![Image of norwegian electra](https://i.imgur.com/QqSEC5I.png)

Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)

Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
# Model
## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators
Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning
- https://openreview.net/pdf?id=r1xMH1BtvB
- https://github.com/google-research/electra
# Acknowledgments
### TensorFlow Research Cloud
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤
- https://www.tensorflow.org/tfrc
#### OSCAR corpus
- https://oscar-corpus.com/
#### OPUS
- http://opus.nlpl.eu/
- http://www.opensubtitles.org/


---
tags:
- conversational
---

#Ally DialoGPT Model


---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
widget:
- text: "Dolar yükselmeye devam ediyor."
  candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
  candidate_labels: "olumlu, olumsuz"
---


---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
  candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
  candidate_labels: "olumlu, olumsuz"
---

<!-- 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-turkish-cased_allnli_tr

This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6481
- Accuracy: 0.7381

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.94          | 0.03  | 1000  | 0.9074          | 0.5813   |
| 0.8102        | 0.07  | 2000  | 0.8802          | 0.5949   |
| 0.7737        | 0.1   | 3000  | 0.8491          | 0.6155   |
| 0.7576        | 0.14  | 4000  | 0.8283          | 0.6261   |
| 0.7286        | 0.17  | 5000  | 0.8150          | 0.6362   |
| 0.7162        | 0.2   | 6000  | 0.7998          | 0.6400   |
| 0.7092        | 0.24  | 7000  | 0.7830          | 0.6565   |
| 0.6962        | 0.27  | 8000  | 0.7653          | 0.6629   |
| 0.6876        | 0.31  | 9000  | 0.7630          | 0.6687   |
| 0.6778        | 0.34  | 10000 | 0.7475          | 0.6739   |
| 0.6737        | 0.37  | 11000 | 0.7495          | 0.6781   |
| 0.6712        | 0.41  | 12000 | 0.7350          | 0.6826   |
| 0.6559        | 0.44  | 13000 | 0.7274          | 0.6897   |
| 0.6493        | 0.48  | 14000 | 0.7248          | 0.6902   |
| 0.6483        | 0.51  | 15000 | 0.7263          | 0.6858   |
| 0.6445        | 0.54  | 16000 | 0.7070          | 0.6978   |
| 0.6467        | 0.58  | 17000 | 0.7083          | 0.6981   |
| 0.6332        | 0.61  | 18000 | 0.6996          | 0.7004   |
| 0.6288        | 0.65  | 19000 | 0.6979          | 0.6978   |
| 0.6308        | 0.68  | 20000 | 0.6912          | 0.7040   |
| 0.622         | 0.71  | 21000 | 0.6904          | 0.7092   |
| 0.615         | 0.75  | 22000 | 0.6872          | 0.7094   |
| 0.6186        | 0.78  | 23000 | 0.6877          | 0.7075   |
| 0.6183        | 0.82  | 24000 | 0.6818          | 0.7111   |
| 0.6115        | 0.85  | 25000 | 0.6856          | 0.7122   |
| 0.608         | 0.88  | 26000 | 0.6697          | 0.7179   |
| 0.6071        | 0.92  | 27000 | 0.6727          | 0.7181   |
| 0.601         | 0.95  | 28000 | 0.6798          | 0.7118   |
| 0.6018        | 0.99  | 29000 | 0.6854          | 0.7071   |
| 0.5762        | 1.02  | 30000 | 0.6697          | 0.7214   |
| 0.5507        | 1.05  | 31000 | 0.6710          | 0.7185   |
| 0.5575        | 1.09  | 32000 | 0.6709          | 0.7226   |
| 0.5493        | 1.12  | 33000 | 0.6659          | 0.7191   |
| 0.5464        | 1.15  | 34000 | 0.6709          | 0.7232   |
| 0.5595        | 1.19  | 35000 | 0.6642          | 0.7220   |
| 0.5446        | 1.22  | 36000 | 0.6709          | 0.7202   |
| 0.5524        | 1.26  | 37000 | 0.6751          | 0.7148   |
| 0.5473        | 1.29  | 38000 | 0.6642          | 0.7209   |
| 0.5477        | 1.32  | 39000 | 0.6662          | 0.7223   |
| 0.5522        | 1.36  | 40000 | 0.6586          | 0.7227   |
| 0.5406        | 1.39  | 41000 | 0.6602          | 0.7258   |
| 0.54          | 1.43  | 42000 | 0.6564          | 0.7273   |
| 0.5458        | 1.46  | 43000 | 0.6780          | 0.7213   |
| 0.5448        | 1.49  | 44000 | 0.6561          | 0.7235   |
| 0.5418        | 1.53  | 45000 | 0.6600          | 0.7253   |
| 0.5408        | 1.56  | 46000 | 0.6616          | 0.7274   |
| 0.5451        | 1.6   | 47000 | 0.6557          | 0.7283   |
| 0.5385        | 1.63  | 48000 | 0.6583          | 0.7295   |
| 0.5261        | 1.66  | 49000 | 0.6468          | 0.7325   |
| 0.5364        | 1.7   | 50000 | 0.6447          | 0.7329   |
| 0.5294        | 1.73  | 51000 | 0.6429          | 0.7320   |
| 0.5332        | 1.77  | 52000 | 0.6508          | 0.7272   |
| 0.5274        | 1.8   | 53000 | 0.6492          | 0.7326   |
| 0.5286        | 1.83  | 54000 | 0.6470          | 0.7318   |
| 0.5359        | 1.87  | 55000 | 0.6393          | 0.7354   |
| 0.5366        | 1.9   | 56000 | 0.6445          | 0.7367   |
| 0.5296        | 1.94  | 57000 | 0.6413          | 0.7313   |
| 0.5346        | 1.97  | 58000 | 0.6393          | 0.7315   |
| 0.5264        | 2.0   | 59000 | 0.6448          | 0.7357   |
| 0.4857        | 2.04  | 60000 | 0.6640          | 0.7335   |
| 0.4888        | 2.07  | 61000 | 0.6612          | 0.7318   |
| 0.4964        | 2.11  | 62000 | 0.6516          | 0.7337   |
| 0.493         | 2.14  | 63000 | 0.6503          | 0.7356   |
| 0.4961        | 2.17  | 64000 | 0.6519          | 0.7348   |
| 0.4847        | 2.21  | 65000 | 0.6517          | 0.7327   |
| 0.483         | 2.24  | 66000 | 0.6555          | 0.7310   |
| 0.4857        | 2.28  | 67000 | 0.6525          | 0.7312   |
| 0.484         | 2.31  | 68000 | 0.6444          | 0.7342   |
| 0.4792        | 2.34  | 69000 | 0.6508          | 0.7330   |
| 0.488         | 2.38  | 70000 | 0.6513          | 0.7344   |
| 0.472         | 2.41  | 71000 | 0.6547          | 0.7346   |
| 0.4872        | 2.45  | 72000 | 0.6500          | 0.7342   |
| 0.4782        | 2.48  | 73000 | 0.6585          | 0.7358   |
| 0.481         | 2.51  | 74000 | 0.6477          | 0.7356   |
| 0.4822        | 2.55  | 75000 | 0.6587          | 0.7346   |
| 0.4728        | 2.58  | 76000 | 0.6572          | 0.7340   |
| 0.4841        | 2.62  | 77000 | 0.6443          | 0.7374   |
| 0.4885        | 2.65  | 78000 | 0.6494          | 0.7362   |
| 0.4752        | 2.68  | 79000 | 0.6509          | 0.7382   |
| 0.4883        | 2.72  | 80000 | 0.6457          | 0.7371   |
| 0.4888        | 2.75  | 81000 | 0.6497          | 0.7364   |
| 0.4844        | 2.79  | 82000 | 0.6481          | 0.7376   |
| 0.4833        | 2.82  | 83000 | 0.6451          | 0.7389   |
| 0.48          | 2.85  | 84000 | 0.6423          | 0.7373   |
| 0.4832        | 2.89  | 85000 | 0.6477          | 0.7357   |
| 0.4805        | 2.92  | 86000 | 0.6464          | 0.7379   |
| 0.4775        | 2.96  | 87000 | 0.6477          | 0.7380   |
| 0.4843        | 2.99  | 88000 | 0.6481          | 0.7381   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: mit
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
  candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
  candidate_labels: "olumlu, olumsuz"
---

<!-- 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-base-turkish-cased_allnli_tr

This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771
- Accuracy: 0.7978

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559        | 0.03  | 1000  | 0.7577          | 0.6798   |
| 0.6612        | 0.07  | 2000  | 0.7263          | 0.6958   |
| 0.6115        | 0.1   | 3000  | 0.6431          | 0.7364   |
| 0.5916        | 0.14  | 4000  | 0.6347          | 0.7407   |
| 0.5719        | 0.17  | 5000  | 0.6317          | 0.7483   |
| 0.5575        | 0.2   | 6000  | 0.6034          | 0.7544   |
| 0.5521        | 0.24  | 7000  | 0.6148          | 0.7568   |
| 0.5393        | 0.27  | 8000  | 0.5931          | 0.7610   |
| 0.5382        | 0.31  | 9000  | 0.5866          | 0.7665   |
| 0.5306        | 0.34  | 10000 | 0.5881          | 0.7594   |
| 0.5295        | 0.37  | 11000 | 0.6120          | 0.7632   |
| 0.5225        | 0.41  | 12000 | 0.5620          | 0.7759   |
| 0.5112        | 0.44  | 13000 | 0.5641          | 0.7769   |
| 0.5133        | 0.48  | 14000 | 0.5571          | 0.7798   |
| 0.5023        | 0.51  | 15000 | 0.5719          | 0.7722   |
| 0.5017        | 0.54  | 16000 | 0.5482          | 0.7844   |
| 0.5111        | 0.58  | 17000 | 0.5503          | 0.7800   |
| 0.4929        | 0.61  | 18000 | 0.5502          | 0.7836   |
| 0.4923        | 0.65  | 19000 | 0.5424          | 0.7843   |
| 0.4894        | 0.68  | 20000 | 0.5417          | 0.7851   |
| 0.4877        | 0.71  | 21000 | 0.5514          | 0.7841   |
| 0.4818        | 0.75  | 22000 | 0.5494          | 0.7848   |
| 0.4898        | 0.78  | 23000 | 0.5450          | 0.7859   |
| 0.4823        | 0.82  | 24000 | 0.5417          | 0.7878   |
| 0.4806        | 0.85  | 25000 | 0.5354          | 0.7875   |
| 0.4779        | 0.88  | 26000 | 0.5338          | 0.7848   |
| 0.4744        | 0.92  | 27000 | 0.5277          | 0.7934   |
| 0.4678        | 0.95  | 28000 | 0.5507          | 0.7871   |
| 0.4727        | 0.99  | 29000 | 0.5603          | 0.7789   |
| 0.4243        | 1.02  | 30000 | 0.5626          | 0.7894   |
| 0.3955        | 1.05  | 31000 | 0.5324          | 0.7939   |
| 0.4022        | 1.09  | 32000 | 0.5322          | 0.7925   |
| 0.3976        | 1.12  | 33000 | 0.5450          | 0.7920   |
| 0.3913        | 1.15  | 34000 | 0.5464          | 0.7948   |
| 0.406         | 1.19  | 35000 | 0.5406          | 0.7958   |
| 0.3875        | 1.22  | 36000 | 0.5489          | 0.7878   |
| 0.4024        | 1.26  | 37000 | 0.5427          | 0.7925   |
| 0.3988        | 1.29  | 38000 | 0.5335          | 0.7904   |
| 0.393         | 1.32  | 39000 | 0.5415          | 0.7923   |
| 0.3988        | 1.36  | 40000 | 0.5385          | 0.7962   |
| 0.3912        | 1.39  | 41000 | 0.5383          | 0.7950   |
| 0.3949        | 1.43  | 42000 | 0.5415          | 0.7931   |
| 0.3902        | 1.46  | 43000 | 0.5438          | 0.7893   |
| 0.3948        | 1.49  | 44000 | 0.5348          | 0.7906   |
| 0.3921        | 1.53  | 45000 | 0.5361          | 0.7890   |
| 0.3944        | 1.56  | 46000 | 0.5419          | 0.7953   |
| 0.3959        | 1.6   | 47000 | 0.5402          | 0.7967   |
| 0.3926        | 1.63  | 48000 | 0.5429          | 0.7925   |
| 0.3854        | 1.66  | 49000 | 0.5346          | 0.7959   |
| 0.3864        | 1.7   | 50000 | 0.5241          | 0.7979   |
| 0.385         | 1.73  | 51000 | 0.5149          | 0.8002   |
| 0.3871        | 1.77  | 52000 | 0.5325          | 0.8002   |
| 0.3819        | 1.8   | 53000 | 0.5332          | 0.8022   |
| 0.384         | 1.83  | 54000 | 0.5419          | 0.7873   |
| 0.3899        | 1.87  | 55000 | 0.5225          | 0.7974   |
| 0.3894        | 1.9   | 56000 | 0.5358          | 0.7977   |
| 0.3838        | 1.94  | 57000 | 0.5264          | 0.7988   |
| 0.3881        | 1.97  | 58000 | 0.5280          | 0.7956   |
| 0.3756        | 2.0   | 59000 | 0.5601          | 0.7969   |
| 0.3156        | 2.04  | 60000 | 0.5936          | 0.7925   |
| 0.3125        | 2.07  | 61000 | 0.5898          | 0.7938   |
| 0.3179        | 2.11  | 62000 | 0.5591          | 0.7981   |
| 0.315         | 2.14  | 63000 | 0.5853          | 0.7970   |
| 0.3122        | 2.17  | 64000 | 0.5802          | 0.7979   |
| 0.3105        | 2.21  | 65000 | 0.5758          | 0.7979   |
| 0.3076        | 2.24  | 66000 | 0.5685          | 0.7980   |
| 0.3117        | 2.28  | 67000 | 0.5799          | 0.7944   |
| 0.3108        | 2.31  | 68000 | 0.5742          | 0.7988   |
| 0.3047        | 2.34  | 69000 | 0.5907          | 0.7921   |
| 0.3114        | 2.38  | 70000 | 0.5723          | 0.7937   |
| 0.3035        | 2.41  | 71000 | 0.5944          | 0.7955   |
| 0.3129        | 2.45  | 72000 | 0.5838          | 0.7928   |
| 0.3071        | 2.48  | 73000 | 0.5929          | 0.7949   |
| 0.3061        | 2.51  | 74000 | 0.5794          | 0.7967   |
| 0.3068        | 2.55  | 75000 | 0.5892          | 0.7954   |
| 0.3053        | 2.58  | 76000 | 0.5796          | 0.7962   |
| 0.3117        | 2.62  | 77000 | 0.5763          | 0.7981   |
| 0.3062        | 2.65  | 78000 | 0.5852          | 0.7964   |
| 0.3004        | 2.68  | 79000 | 0.5793          | 0.7966   |
| 0.3146        | 2.72  | 80000 | 0.5693          | 0.7985   |
| 0.3146        | 2.75  | 81000 | 0.5788          | 0.7982   |
| 0.3079        | 2.79  | 82000 | 0.5726          | 0.7978   |
| 0.3058        | 2.82  | 83000 | 0.5677          | 0.7988   |
| 0.3055        | 2.85  | 84000 | 0.5701          | 0.7982   |
| 0.3049        | 2.89  | 85000 | 0.5809          | 0.7970   |
| 0.3044        | 2.92  | 86000 | 0.5741          | 0.7986   |
| 0.3057        | 2.96  | 87000 | 0.5743          | 0.7980   |
| 0.3081        | 2.99  | 88000 | 0.5771          | 0.7978   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: mit
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
  candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
  candidate_labels: "olumlu, olumsuz"
---

<!-- 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-base-multilingual-cased_allnli_tr

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6144
- Accuracy: 0.7662

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8623        | 0.03  | 1000  | 0.9076          | 0.5917   |
| 0.7528        | 0.07  | 2000  | 0.8587          | 0.6119   |
| 0.7074        | 0.1   | 3000  | 0.7867          | 0.6647   |
| 0.6949        | 0.14  | 4000  | 0.7474          | 0.6772   |
| 0.6681        | 0.17  | 5000  | 0.7661          | 0.6814   |
| 0.6597        | 0.2   | 6000  | 0.7264          | 0.6943   |
| 0.6495        | 0.24  | 7000  | 0.7841          | 0.6781   |
| 0.6323        | 0.27  | 8000  | 0.7256          | 0.6952   |
| 0.6308        | 0.31  | 9000  | 0.7319          | 0.6958   |
| 0.6254        | 0.34  | 10000 | 0.7054          | 0.7004   |
| 0.6233        | 0.37  | 11000 | 0.7069          | 0.7085   |
| 0.6165        | 0.41  | 12000 | 0.6880          | 0.7181   |
| 0.6033        | 0.44  | 13000 | 0.6844          | 0.7197   |
| 0.6014        | 0.48  | 14000 | 0.6753          | 0.7129   |
| 0.5947        | 0.51  | 15000 | 0.7000          | 0.7039   |
| 0.5965        | 0.54  | 16000 | 0.6708          | 0.7263   |
| 0.5979        | 0.58  | 17000 | 0.6562          | 0.7285   |
| 0.5787        | 0.61  | 18000 | 0.6554          | 0.7297   |
| 0.58          | 0.65  | 19000 | 0.6544          | 0.7315   |
| 0.574         | 0.68  | 20000 | 0.6549          | 0.7339   |
| 0.5751        | 0.71  | 21000 | 0.6545          | 0.7289   |
| 0.5659        | 0.75  | 22000 | 0.6467          | 0.7371   |
| 0.5732        | 0.78  | 23000 | 0.6448          | 0.7362   |
| 0.5637        | 0.82  | 24000 | 0.6520          | 0.7355   |
| 0.5648        | 0.85  | 25000 | 0.6412          | 0.7345   |
| 0.5622        | 0.88  | 26000 | 0.6350          | 0.7358   |
| 0.5579        | 0.92  | 27000 | 0.6347          | 0.7393   |
| 0.5518        | 0.95  | 28000 | 0.6417          | 0.7392   |
| 0.5547        | 0.99  | 29000 | 0.6321          | 0.7437   |
| 0.524         | 1.02  | 30000 | 0.6430          | 0.7412   |
| 0.4982        | 1.05  | 31000 | 0.6253          | 0.7458   |
| 0.5002        | 1.09  | 32000 | 0.6316          | 0.7418   |
| 0.4993        | 1.12  | 33000 | 0.6197          | 0.7487   |
| 0.4963        | 1.15  | 34000 | 0.6307          | 0.7462   |
| 0.504         | 1.19  | 35000 | 0.6272          | 0.7480   |
| 0.4922        | 1.22  | 36000 | 0.6410          | 0.7433   |
| 0.5016        | 1.26  | 37000 | 0.6295          | 0.7461   |
| 0.4957        | 1.29  | 38000 | 0.6183          | 0.7506   |
| 0.4883        | 1.32  | 39000 | 0.6261          | 0.7502   |
| 0.4985        | 1.36  | 40000 | 0.6315          | 0.7496   |
| 0.4885        | 1.39  | 41000 | 0.6189          | 0.7529   |
| 0.4909        | 1.43  | 42000 | 0.6189          | 0.7473   |
| 0.4894        | 1.46  | 43000 | 0.6314          | 0.7433   |
| 0.4912        | 1.49  | 44000 | 0.6184          | 0.7446   |
| 0.4851        | 1.53  | 45000 | 0.6258          | 0.7461   |
| 0.4879        | 1.56  | 46000 | 0.6286          | 0.7480   |
| 0.4907        | 1.6   | 47000 | 0.6196          | 0.7512   |
| 0.4884        | 1.63  | 48000 | 0.6157          | 0.7526   |
| 0.4755        | 1.66  | 49000 | 0.6056          | 0.7591   |
| 0.4811        | 1.7   | 50000 | 0.5977          | 0.7582   |
| 0.4787        | 1.73  | 51000 | 0.5915          | 0.7621   |
| 0.4779        | 1.77  | 52000 | 0.6014          | 0.7583   |
| 0.4767        | 1.8   | 53000 | 0.6041          | 0.7623   |
| 0.4737        | 1.83  | 54000 | 0.6093          | 0.7563   |
| 0.4836        | 1.87  | 55000 | 0.6001          | 0.7568   |
| 0.4765        | 1.9   | 56000 | 0.6109          | 0.7601   |
| 0.4776        | 1.94  | 57000 | 0.6046          | 0.7599   |
| 0.4769        | 1.97  | 58000 | 0.5970          | 0.7568   |
| 0.4654        | 2.0   | 59000 | 0.6147          | 0.7614   |
| 0.4144        | 2.04  | 60000 | 0.6439          | 0.7566   |
| 0.4101        | 2.07  | 61000 | 0.6373          | 0.7527   |
| 0.4192        | 2.11  | 62000 | 0.6136          | 0.7575   |
| 0.4128        | 2.14  | 63000 | 0.6283          | 0.7560   |
| 0.4204        | 2.17  | 64000 | 0.6187          | 0.7625   |
| 0.4114        | 2.21  | 65000 | 0.6127          | 0.7621   |
| 0.4097        | 2.24  | 66000 | 0.6188          | 0.7626   |
| 0.4129        | 2.28  | 67000 | 0.6156          | 0.7639   |
| 0.4085        | 2.31  | 68000 | 0.6232          | 0.7616   |
| 0.4074        | 2.34  | 69000 | 0.6240          | 0.7605   |
| 0.409         | 2.38  | 70000 | 0.6153          | 0.7591   |
| 0.4046        | 2.41  | 71000 | 0.6375          | 0.7587   |
| 0.4117        | 2.45  | 72000 | 0.6145          | 0.7629   |
| 0.4002        | 2.48  | 73000 | 0.6279          | 0.7610   |
| 0.4042        | 2.51  | 74000 | 0.6176          | 0.7646   |
| 0.4055        | 2.55  | 75000 | 0.6277          | 0.7643   |
| 0.4021        | 2.58  | 76000 | 0.6196          | 0.7642   |
| 0.4081        | 2.62  | 77000 | 0.6127          | 0.7659   |
| 0.408         | 2.65  | 78000 | 0.6237          | 0.7638   |
| 0.3997        | 2.68  | 79000 | 0.6190          | 0.7636   |
| 0.4093        | 2.72  | 80000 | 0.6152          | 0.7648   |
| 0.4095        | 2.75  | 81000 | 0.6155          | 0.7627   |
| 0.4088        | 2.79  | 82000 | 0.6130          | 0.7641   |
| 0.4063        | 2.82  | 83000 | 0.6072          | 0.7646   |
| 0.3978        | 2.85  | 84000 | 0.6128          | 0.7662   |
| 0.4034        | 2.89  | 85000 | 0.6157          | 0.7627   |
| 0.4044        | 2.92  | 86000 | 0.6127          | 0.7661   |
| 0.403         | 2.96  | 87000 | 0.6126          | 0.7664   |
| 0.4033        | 2.99  | 88000 | 0.6144          | 0.7662   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
  candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
  candidate_labels: "olumlu, olumsuz"
---

<!-- 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. -->

# convbert-base-turkish-mc4-cased_allnli_tr

This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/dbmdz/convbert-base-turkish-mc4-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5541
- Accuracy: 0.8111

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7338        | 0.03  | 1000  | 0.6722          | 0.7236   |
| 0.603         | 0.07  | 2000  | 0.6465          | 0.7399   |
| 0.5605        | 0.1   | 3000  | 0.5801          | 0.7728   |
| 0.55          | 0.14  | 4000  | 0.5994          | 0.7626   |
| 0.529         | 0.17  | 5000  | 0.5720          | 0.7697   |
| 0.5196        | 0.2   | 6000  | 0.5692          | 0.7769   |
| 0.5117        | 0.24  | 7000  | 0.5725          | 0.7785   |
| 0.5044        | 0.27  | 8000  | 0.5532          | 0.7787   |
| 0.5016        | 0.31  | 9000  | 0.5546          | 0.7812   |
| 0.5031        | 0.34  | 10000 | 0.5461          | 0.7870   |
| 0.4949        | 0.37  | 11000 | 0.5725          | 0.7826   |
| 0.4894        | 0.41  | 12000 | 0.5419          | 0.7933   |
| 0.4796        | 0.44  | 13000 | 0.5278          | 0.7914   |
| 0.4795        | 0.48  | 14000 | 0.5193          | 0.7953   |
| 0.4713        | 0.51  | 15000 | 0.5534          | 0.7771   |
| 0.4738        | 0.54  | 16000 | 0.5098          | 0.8039   |
| 0.481         | 0.58  | 17000 | 0.5244          | 0.7958   |
| 0.4634        | 0.61  | 18000 | 0.5215          | 0.7972   |
| 0.465         | 0.65  | 19000 | 0.5129          | 0.7985   |
| 0.4624        | 0.68  | 20000 | 0.5062          | 0.8047   |
| 0.4597        | 0.71  | 21000 | 0.5114          | 0.8029   |
| 0.4571        | 0.75  | 22000 | 0.5070          | 0.8073   |
| 0.4602        | 0.78  | 23000 | 0.5115          | 0.7993   |
| 0.4552        | 0.82  | 24000 | 0.5085          | 0.8052   |
| 0.4538        | 0.85  | 25000 | 0.5118          | 0.7974   |
| 0.4517        | 0.88  | 26000 | 0.5036          | 0.8044   |
| 0.4517        | 0.92  | 27000 | 0.4930          | 0.8062   |
| 0.4413        | 0.95  | 28000 | 0.5307          | 0.7964   |
| 0.4483        | 0.99  | 29000 | 0.5195          | 0.7938   |
| 0.4036        | 1.02  | 30000 | 0.5238          | 0.8029   |
| 0.3724        | 1.05  | 31000 | 0.5125          | 0.8082   |
| 0.3777        | 1.09  | 32000 | 0.5099          | 0.8075   |
| 0.3753        | 1.12  | 33000 | 0.5172          | 0.8053   |
| 0.367         | 1.15  | 34000 | 0.5188          | 0.8053   |
| 0.3819        | 1.19  | 35000 | 0.5218          | 0.8046   |
| 0.363         | 1.22  | 36000 | 0.5202          | 0.7993   |
| 0.3794        | 1.26  | 37000 | 0.5240          | 0.8048   |
| 0.3749        | 1.29  | 38000 | 0.5026          | 0.8054   |
| 0.367         | 1.32  | 39000 | 0.5198          | 0.8075   |
| 0.3759        | 1.36  | 40000 | 0.5298          | 0.7993   |
| 0.3701        | 1.39  | 41000 | 0.5072          | 0.8091   |
| 0.3742        | 1.43  | 42000 | 0.5071          | 0.8098   |
| 0.3706        | 1.46  | 43000 | 0.5317          | 0.8037   |
| 0.3716        | 1.49  | 44000 | 0.5034          | 0.8052   |
| 0.3717        | 1.53  | 45000 | 0.5258          | 0.8012   |
| 0.3714        | 1.56  | 46000 | 0.5195          | 0.8050   |
| 0.3781        | 1.6   | 47000 | 0.5004          | 0.8104   |
| 0.3725        | 1.63  | 48000 | 0.5124          | 0.8113   |
| 0.3624        | 1.66  | 49000 | 0.5040          | 0.8094   |
| 0.3657        | 1.7   | 50000 | 0.4979          | 0.8111   |
| 0.3669        | 1.73  | 51000 | 0.4968          | 0.8100   |
| 0.3636        | 1.77  | 52000 | 0.5075          | 0.8079   |
| 0.36          | 1.8   | 53000 | 0.4985          | 0.8110   |
| 0.3624        | 1.83  | 54000 | 0.5125          | 0.8070   |
| 0.366         | 1.87  | 55000 | 0.4918          | 0.8117   |
| 0.3655        | 1.9   | 56000 | 0.5051          | 0.8109   |
| 0.3609        | 1.94  | 57000 | 0.5083          | 0.8105   |
| 0.3672        | 1.97  | 58000 | 0.5129          | 0.8085   |
| 0.3545        | 2.0   | 59000 | 0.5467          | 0.8109   |
| 0.2938        | 2.04  | 60000 | 0.5635          | 0.8049   |
| 0.29          | 2.07  | 61000 | 0.5781          | 0.8041   |
| 0.2992        | 2.11  | 62000 | 0.5470          | 0.8077   |
| 0.2957        | 2.14  | 63000 | 0.5765          | 0.8073   |
| 0.292         | 2.17  | 64000 | 0.5472          | 0.8106   |
| 0.2893        | 2.21  | 65000 | 0.5590          | 0.8085   |
| 0.2883        | 2.24  | 66000 | 0.5535          | 0.8064   |
| 0.2923        | 2.28  | 67000 | 0.5508          | 0.8095   |
| 0.2868        | 2.31  | 68000 | 0.5679          | 0.8098   |
| 0.2892        | 2.34  | 69000 | 0.5660          | 0.8057   |
| 0.292         | 2.38  | 70000 | 0.5494          | 0.8088   |
| 0.286         | 2.41  | 71000 | 0.5653          | 0.8085   |
| 0.2939        | 2.45  | 72000 | 0.5673          | 0.8070   |
| 0.286         | 2.48  | 73000 | 0.5600          | 0.8092   |
| 0.2844        | 2.51  | 74000 | 0.5508          | 0.8095   |
| 0.2913        | 2.55  | 75000 | 0.5645          | 0.8088   |
| 0.2859        | 2.58  | 76000 | 0.5677          | 0.8095   |
| 0.2892        | 2.62  | 77000 | 0.5598          | 0.8113   |
| 0.2898        | 2.65  | 78000 | 0.5618          | 0.8096   |
| 0.2814        | 2.68  | 79000 | 0.5664          | 0.8103   |
| 0.2917        | 2.72  | 80000 | 0.5484          | 0.8122   |
| 0.2907        | 2.75  | 81000 | 0.5522          | 0.8116   |
| 0.2896        | 2.79  | 82000 | 0.5540          | 0.8093   |
| 0.2907        | 2.82  | 83000 | 0.5469          | 0.8104   |
| 0.2882        | 2.85  | 84000 | 0.5471          | 0.8122   |
| 0.2878        | 2.89  | 85000 | 0.5532          | 0.8108   |
| 0.2858        | 2.92  | 86000 | 0.5511          | 0.8115   |
| 0.288         | 2.96  | 87000 | 0.5491          | 0.8111   |
| 0.2834        | 2.99  | 88000 | 0.5541          | 0.8111   |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


---
language:
- tr
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- nli_tr
- emrecan/stsb-mt-turkish
widget:
  source_sentence: "Bu çok mutlu bir kişi"
  sentences:
    - "Bu mutlu bir köpek"
    - "Bu sevincinden havalara uçan bir insan"
    - "Çok kar yağıyor"

---

# emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

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. The model was trained on Turkish machine translated versions of [NLI](https://huggingface.co/datasets/nli_tr) and [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) datasets, using example [training scripts]( https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) from sentence-transformers GitHub repository.

## 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 = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]

model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
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 = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')

# 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

Evaluation results on test and development sets are given below:

| Split      | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
|------------|-------|----------------|-----------------|-------------------|--------------------|-------------------|--------------------|-------------|--------------|
| test       |   -   | 0.834          | 0.830           | 0.820             | 0.819              | 0.819             | 0.818              | 0.799       | 0.789        |
| validation | 1     | 0.850          | 0.848           | 0.831             | 0.835              | 0.83              | 0.83               | 0.80        | 0.806        |
| validation | 2     | 0.857          | 0.857           | 0.844             | 0.848              | 0.844             | 0.848              | 0.813       | 0.810        |
| validation | 3     | 0.860          | 0.859           | 0.846             | 0.851              | 0.846             | 0.850              | 0.825       | 0.822        |
| validation | 4     | 0.859          | 0.860           | 0.846             | 0.851              | 0.846             | 0.851              | 0.825       | 0.823        |

## Training
Training scripts [`training_nli_v2.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py) and [`training_stsbenchmark_continue_training.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) were used to train the model.

The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

Parameters of the fit()-Method:
```
{
    "epochs": 4,
    "evaluation_steps": 200,
    "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': 75, 'do_lower_case': False}) with Transformer model: BertModel
  (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

<!--- Describe where people can find more information -->


---

thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png

tags:

- conversational

license: mit

---

# DialoGPT Trained on the Speech of a Game Character

This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).

Chat with the model:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-small-neku")

model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-small-neku")

# Let's chat for 4 lines
for step in range(4):
    # 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')
    # print(new_user_input_ids)

    # 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("NekuBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```


---

thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png

tags:

- conversational

license: mit

---

# DialoGPT Trained on the Speech of a Game Character

This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).

Chat with the model:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("Zane/Ricky")

model = AutoModelWithLMHead.from_pretrained("Zane/Ricky")

# Let's chat for 4 lines
for step in range(4):
    # 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')
    # print(new_user_input_ids)

    # 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("NekuBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```


---
tags:
- fastai
---

# TODO: Fill this model card with more information about your fastai model!


---
tags:
- fastai
---

Congratulations on hosting your fastai model on the 🤗Hub!

# Some next steps
1. Fill out this model card with more information ([documentation here](https://huggingface.co/docs/hub/model-repos))!

2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).

3. Join our fastai community on the Hugging Face Discord!

Greetings fellow fastlearner 🤝!


---
tags:
- image-classification
- fastai
library_name: fastai
datasets:
- Oxford-IIIT Pet Dataset
- ImageNet
---

## Pet breeds classification model

Finetuned model on The Oxford-IIIT Pet Dataset. It was introduced in
[this paper](https://www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/) and first released in
[this webpage](https://www.robots.ox.ac.uk/~vgg/data/pets/).

The pretrained model was trained on the ImageNet dataset, a dataset that has 100,000+ images across 200 different classes. It was introduced in [this paper](https://image-net.org/static_files/papers/imagenet_cvpr09.pdf) and available [in this webpage](https://image-net.org/download.php)

Disclaimer: The model was fine-tuned after [Chapter 5](https://github.com/fastai/fastbook/blob/master/05_pet_breeds.ipynb) of [Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD (2020)](https://github.com/fastai/fastbook) written by Jeremy Howard and Sylvain Gugger.

## Model description

The model was finetuned using the `cnn_learner` method of the fastai library suing a Resnet 34 backbone pretrained on the ImageNet dataset. The fastai library uses PyTorch for the undelying operations. `cnn_learner` automatically gets a pretrained model from a given architecture with a custom head that is suitable for the target data.

Resnet34 is a 34 layer convolutional neural network. It takes residuals from each layer and uses them in the subsequent connected layers. Advantages of a resnet arquitecture ([Neurohive, 2019](https://neurohive.io/en/popular-networks/resnet/)):
- Are easy to optimize, but the “plain” networks (that simply stack layers) shows higher training error when the depth increases.
- Can easily gain accuracy from greatly increased depth, producing results which are better than previous networks.

 Please refer to the original paper '[Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf)' written by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun.

Specifically, the model was obtained:

```
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(2)
```

## How to use

Download the model this way:
```python
from huggingface_hub import hf_hub_download
from fastai.learner import load_learner

model = load_learner(
    hf_hub_download('espejelomar/fastai-pet-breeds-classification', filename="model.pkl")
    )
```

Then you can use your downloaded fastai model in any way you want. For example, if the input is a PIL Image, with the following code you can obtain the resulting outputs for each class:

```python
_, _, preds = self.model.predict(np.array(inputs))
```

## Training data

The Resnet34 model was pretrained on [ImageNet](https://image-net.org/static_files/papers/imagenet_cvpr09.pdf), a dataset that has 100,000+ images across 200 different classes, and fine-tuned on [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/).

## Preprocessing

For more detailed information on the preprocessing procedure, refer to the [Chapter 5](https://github.com/fastai/fastbook/blob/master/05_pet_breeds.ipynb) of [Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD (2020)](https://github.com/fastai/fastbook).

Two main strategies are followed to presizing the images:

- Resize images to relatively "large" dimensions—that is, dimensions significantly larger than the target training dimensions.
- Compose all of the common augmentation operations (including a resize to the final target size) into one, and perform the combined operation on the GPU only once at the end of processing, rather than performing the operations individually and interpolating multiple times.

"The first step, the resize, creates images large enough that they have spare margin to allow further augmentation transforms on their inner regions without creating empty zones. This transformation works by resizing to a square, using a large crop size. On the training set, the crop area is chosen randomly, and the size of the crop is selected to cover the entire width or height of the image, whichever is smaller.

In the second step, the GPU is used for all data augmentation, and all of the potentially destructive operations are done together, with a single interpolation at the end." ([Howard and Gugger, 2020](https://github.com/fastai/fastbook))

Specifically, the following code is used for preprocessing:

```python
#hide_input
#id interpolations
#caption A comparison of fastai's data augmentation strategy (left) and the traditional approach (right).
dblock1 = DataBlock(blocks=(ImageBlock(), CategoryBlock()),
                   get_y=parent_label,
                   item_tfms=Resize(460))
# Place an image in the 'images/grizzly.jpg' subfolder where this notebook is located before running this
dls1 = dblock1.dataloaders([(Path.cwd()/'images'/'grizzly.jpg')]*100, bs=8)
dls1.train.get_idxs = lambda: Inf.ones
x,y = dls1.valid.one_batch()
_,axs = subplots(1, 2)

x1 = TensorImage(x.clone())
x1 = x1.affine_coord(sz=224)
x1 = x1.rotate(draw=30, p=1.)
x1 = x1.zoom(draw=1.2, p=1.)
x1 = x1.warp(draw_x=-0.2, draw_y=0.2, p=1.)

tfms = setup_aug_tfms([Rotate(draw=30, p=1, size=224), Zoom(draw=1.2, p=1., size=224),
                       Warp(draw_x=-0.2, draw_y=0.2, p=1., size=224)])
x = Pipeline(tfms)(x)
#x.affine_coord(coord_tfm=coord_tfm, sz=size, mode=mode, pad_mode=pad_mode)
TensorImage(x[0]).show(ctx=axs[0])
TensorImage(x1[0]).show(ctx=axs[1]);
```

### BibTeX entry and citation info

```bibtex
@book{howard2020deep,
  author    = {Howard, J. and Gugger, S.},
  title     = {Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},
  isbn      = {9781492045526},
  year      = {2020},
  url       = {https://books.google.no/books?id=xd6LxgEACAAJ},
  publisher = {O'Reilly Media, Incorporated},
}
```


RoBERTa model trained on 1M SMILES from PubChem 77M set in MoleculeNet. Uses Smiles-Tokenizer


---
tags:
- conversational
---

# Cartman DialoGPT Model


---
tags:
- conversational
---

# SouthPark Kyle Bot


---
language: de
---

## xlm-roberta-large-finetuned-conll03-german


---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indic-transformers-te-distilbert
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      args: te
    metrics:
    - name: Precision
      type: precision
      value: 0.5657225853304285
    - name: Recall
      type: recall
      value: 0.6486261448792673
    - name: F1
      type: f1
      value: 0.604344453064391
    - name: Accuracy
      type: accuracy
      value: 0.9049186160277506
---

<!-- 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. -->

# indic-transformers-te-distilbert

This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2940
- Precision: 0.5657
- Recall: 0.6486
- F1: 0.6043
- Accuracy: 0.9049

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 125  | 0.3629          | 0.4855    | 0.5287 | 0.5062 | 0.8826   |
| No log        | 2.0   | 250  | 0.3032          | 0.5446    | 0.6303 | 0.5843 | 0.9002   |
| No log        | 3.0   | 375  | 0.2940          | 0.5657    | 0.6486 | 0.6043 | 0.9049   |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


---
language: en
thumbnail:
license: mit
tags:
- question-answering
- mobilebert
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
---

## MobileBERT fine-tuned on SQuAD v1

[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.

This model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).

## Details

| Dataset  | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K      |
| SQuAD1.1 | eval  | 11.1k     |

### Fine-tuning
- Python: `3.7.5`

- Machine specs:

  `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`

  `Memory: 32 GiB`

  `GPUs: 2 GeForce GTX 1070, each with 8GiB memory`

  `GPU driver: 418.87.01, CUDA: 10.1`

- script:

  ```shell
  # after install https://github.com/huggingface/transformers

  cd examples/question-answering
  mkdir -p data

  wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json

  wget -O data/dev-v1.1.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

  export SQUAD_DIR=`pwd`/data

  python run_squad.py \
    --model_type mobilebert \
    --model_name_or_path google/mobilebert-uncased \
    --do_train \
    --do_eval \
    --do_lower_case \
    --train_file $SQUAD_DIR/train-v1.1.json \
    --predict_file $SQUAD_DIR/dev-v1.1.json \
    --per_gpu_train_batch_size 16 \
    --per_gpu_eval_batch_size 16 \
    --learning_rate 4e-5 \
    --num_train_epochs 5.0 \
    --max_seq_length 320 \
    --doc_stride 128 \
    --warmup_steps 1400 \
    --output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v1 2>&1 | tee train-mobilebert-warmup-squad_v1.log
  ```

It took about 3 hours to finish.

### Results

**Model size**: `95M`

| Metric | # Value   | # Original ([Table 5](https://arxiv.org/pdf/2004.02984.pdf))|
| ------ | --------- | --------- |
| **EM** | **82.6** | **82.9** |
| **F1** | **90.0** | **90.0** |

Note that the above results didn't involve any hyperparameter search.

## Example Usage

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/mobilebert-uncased-squad-v1",
    tokenizer="csarron/mobilebert-uncased-squad-v1"
)

predictions = qa_pipeline({
    'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
    'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.7754058241844177, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```

> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)

> Made with ❤ in New York.


---
language: en
thumbnail:
license: mit
tags:
- question-answering
- mobilebert
datasets:
- squad_v2
metrics:
- squad_v2
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
---

## MobileBERT fine-tuned on SQuAD v2

[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.

This model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer).

## Details

| Dataset  | Split | # samples |
| -------- | ----- | --------- |
| SQuAD2.0 | train | 130k      |
| SQuAD2.0 | eval  | 12.3k     |

### Fine-tuning
- Python: `3.7.5`

- Machine specs:

  `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`

  `Memory: 32 GiB`

  `GPUs: 2 GeForce GTX 1070, each with 8GiB memory`

  `GPU driver: 418.87.01, CUDA: 10.1`

- script:

  ```shell
  # after install https://github.com/huggingface/transformers

  cd examples/question-answering
  mkdir -p data

  wget -O data/train-v2.0.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json

  wget -O data/dev-v2.0.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json

  export SQUAD_DIR=`pwd`/data

  python run_squad.py \
    --model_type mobilebert \
    --model_name_or_path google/mobilebert-uncased \
    --do_train \
    --do_eval \
    --do_lower_case \
    --version_2_with_negative \
    --train_file $SQUAD_DIR/train-v2.0.json \
    --predict_file $SQUAD_DIR/dev-v2.0.json \
    --per_gpu_train_batch_size 16 \
    --per_gpu_eval_batch_size 16 \
    --learning_rate 4e-5 \
    --num_train_epochs 5.0 \
    --max_seq_length 320 \
    --doc_stride 128 \
    --warmup_steps 1400 \
    --save_steps 2000 \
    --output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v2 2>&1 | tee train-mobilebert-warmup-squad_v2.log
  ```

It took about 3.5 hours to finish.

### Results

**Model size**: `95M`

| Metric | # Value   | # Original ([Table 5](https://arxiv.org/pdf/2004.02984.pdf))|
| ------ | --------- | --------- |
| **EM** | **75.2** | **76.2** |
| **F1** | **78.8** | **79.2** |

Note that the above results didn't involve any hyperparameter search.

## Example Usage

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/mobilebert-uncased-squad-v2",
    tokenizer="csarron/mobilebert-uncased-squad-v2"
)

predictions = qa_pipeline({
    'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
    'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.71434086561203, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```

> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)

> Made with ❤ in New York.


---
language: en
thumbnail:
license: mit
tags:
- question-answering
- bert
- bert-base
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
---

## BERT-base uncased model fine-tuned on SQuAD v1

This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-insensitive: it does not make a difference between english and English.

## Details

| Dataset  | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K      |
| SQuAD1.1 | eval  | 11.1k     |

### Fine-tuning
- Python: `3.7.5`

- Machine specs:

  `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`

  `Memory: 32 GiB`

  `GPUs: 2 GeForce GTX 1070, each with 8GiB memory`

  `GPU driver: 418.87.01, CUDA: 10.1`

- script:

  ```shell
  # after install https://github.com/huggingface/transformers

  cd examples/question-answering
  mkdir -p data

  wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json

  wget -O data/dev-v1.1.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

  python run_squad.py \
    --model_type bert \
    --model_name_or_path bert-base-uncased \
    --do_train \
    --do_eval \
    --do_lower_case \
    --train_file train-v1.1.json \
    --predict_file dev-v1.1.json \
    --per_gpu_train_batch_size 12 \
    --per_gpu_eval_batch_size=16 \
    --learning_rate 3e-5 \
    --num_train_epochs 2.0 \
    --max_seq_length 320 \
    --doc_stride 128 \
    --data_dir data \
    --output_dir data/bert-base-uncased-squad-v1 2>&1 | tee train-energy-bert-base-squad-v1.log
  ```

It took about 2 hours to finish.

### Results

**Model size**: `418M`

| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **80.9** | **80.8** |
| **F1** | **88.2** | **88.5** |

Note that the above results didn't involve any hyperparameter search.

## Example Usage

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/bert-base-uncased-squad-v1",
    tokenizer="csarron/bert-base-uncased-squad-v1"
)

predictions = qa_pipeline({
    'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
    'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.8730505704879761, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```

> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)

> Made with ❤ in New York.


---
language: en
thumbnail:
license: mit
tags:
- question-answering
- roberta
- roberta-base
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
  context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
---

## RoBERTa-base fine-tuned on SQuAD v1

This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-sensitive: it makes a difference between english and English.

## Details

| Dataset  | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 96.8K      |
| SQuAD1.1 | eval  | 11.8k     |

### Fine-tuning
- Python: `3.7.5`

- Machine specs:

  `CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`

  `Memory: 32 GiB`

  `GPUs: 2 GeForce GTX 1070, each with 8GiB memory`

  `GPU driver: 418.87.01, CUDA: 10.1`

- script:

  ```shell
  # after install https://github.com/huggingface/transformers

  cd examples/question-answering
  mkdir -p data

  wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json

  wget -O data/dev-v1.1.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

  python run_energy_squad.py \
    --model_type roberta \
    --model_name_or_path roberta-base \
    --do_train \
    --do_eval \
    --train_file train-v1.1.json \
    --predict_file dev-v1.1.json \
    --per_gpu_train_batch_size 12 \
    --per_gpu_eval_batch_size 16 \
    --learning_rate 3e-5 \
    --num_train_epochs 2.0 \
    --max_seq_length 320 \
    --doc_stride 128 \
    --data_dir data \
    --output_dir data/roberta-base-squad-v1 2>&1 | tee train-roberta-base-squad-v1.log
  ```

It took about 2 hours to finish.

### Results

**Model size**: `477M`

| Metric | # Value   |
| ------ | --------- |
| **EM** | **83.0** |
| **F1** | **90.4** |

Note that the above results didn't involve any hyperparameter search.

## Example Usage

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/roberta-base-squad-v1",
    tokenizer="csarron/roberta-base-squad-v1"
)

predictions = qa_pipeline({
    'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
    'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.8625259399414062, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```

> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)

> Made with ❤ in New York.


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9328604420983174
    - name: Recall
      type: recall
      value: 0.9516997643890945
    - name: F1
      type: f1
      value: 0.9421859380206598
    - name: Accuracy
      type: accuracy
      value: 0.986342497203744
---

<!-- 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-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.9329
- Recall: 0.9517
- F1: 0.9422
- Accuracy: 0.9863

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0904        | 1.0   | 1756 | 0.0686          | 0.9227    | 0.9355 | 0.9291 | 0.9820   |
| 0.0385        | 2.0   | 3512 | 0.0586          | 0.9381    | 0.9490 | 0.9435 | 0.9862   |
| 0.0215        | 3.0   | 5268 | 0.0612          | 0.9329    | 0.9517 | 0.9422 | 0.9863   |

### Framework versions

- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3