Datasets:

ftopal

/

huggingface-models-raw

like 0

{}

202015004/wav2vec2-base-TLT-Shreya-trial

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6259 - Wer: 0.3544 ## 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6744 | 0.5 | 500 | 2.9473 | 1.0 | | 1.4535 | 1.01 | 1000 | 0.7774 | 0.6254 | | 0.7376 | 1.51 | 1500 | 0.6923 | 0.5712 | | 0.5848 | 2.01 | 2000 | 0.5445 | 0.5023 | | 0.4492 | 2.51 | 2500 | 0.5148 | 0.4958 | | 0.4006 | 3.02 | 3000 | 0.5283 | 0.4781 | | 0.3319 | 3.52 | 3500 | 0.5196 | 0.4628 | | 0.3424 | 4.02 | 4000 | 0.5285 | 0.4551 | | 0.2772 | 4.52 | 4500 | 0.5060 | 0.4532 | | 0.2724 | 5.03 | 5000 | 0.5216 | 0.4422 | | 0.2375 | 5.53 | 5500 | 0.5376 | 0.4443 | | 0.2279 | 6.03 | 6000 | 0.6051 | 0.4308 | | 0.2091 | 6.53 | 6500 | 0.5084 | 0.4423 | | 0.2029 | 7.04 | 7000 | 0.5083 | 0.4242 | | 0.1784 | 7.54 | 7500 | 0.6123 | 0.4297 | | 0.1774 | 8.04 | 8000 | 0.5749 | 0.4339 | | 0.1542 | 8.54 | 8500 | 0.5110 | 0.4033 | | 0.1638 | 9.05 | 9000 | 0.6324 | 0.4318 | | 0.1493 | 9.55 | 9500 | 0.6100 | 0.4152 | | 0.1591 | 10.05 | 10000 | 0.5508 | 0.4022 | | 0.1304 | 10.55 | 10500 | 0.5090 | 0.4054 | | 0.1234 | 11.06 | 11000 | 0.6282 | 0.4093 | | 0.1218 | 11.56 | 11500 | 0.5817 | 0.3941 | | 0.121 | 12.06 | 12000 | 0.5741 | 0.3999 | | 0.1073 | 12.56 | 12500 | 0.5818 | 0.4149 | | 0.104 | 13.07 | 13000 | 0.6492 | 0.3953 | | 0.0934 | 13.57 | 13500 | 0.5393 | 0.4083 | | 0.0961 | 14.07 | 14000 | 0.5510 | 0.3919 | | 0.0965 | 14.57 | 14500 | 0.5896 | 0.3992 | | 0.0921 | 15.08 | 15000 | 0.5554 | 0.3947 | | 0.0751 | 15.58 | 15500 | 0.6312 | 0.3934 | | 0.0805 | 16.08 | 16000 | 0.6732 | 0.3948 | | 0.0742 | 16.58 | 16500 | 0.5990 | 0.3884 | | 0.0708 | 17.09 | 17000 | 0.6186 | 0.3869 | | 0.0679 | 17.59 | 17500 | 0.5837 | 0.3848 | | 0.072 | 18.09 | 18000 | 0.5831 | 0.3775 | | 0.0597 | 18.59 | 18500 | 0.6562 | 0.3843 | | 0.0612 | 19.1 | 19000 | 0.6298 | 0.3756 | | 0.0514 | 19.6 | 19500 | 0.6746 | 0.3720 | | 0.061 | 20.1 | 20000 | 0.6236 | 0.3788 | | 0.054 | 20.6 | 20500 | 0.6012 | 0.3718 | | 0.0521 | 21.11 | 21000 | 0.6053 | 0.3778 | | 0.0494 | 21.61 | 21500 | 0.6154 | 0.3772 | | 0.0468 | 22.11 | 22000 | 0.6052 | 0.3747 | | 0.0413 | 22.61 | 22500 | 0.5877 | 0.3716 | | 0.0424 | 23.12 | 23000 | 0.5786 | 0.3658 | | 0.0403 | 23.62 | 23500 | 0.5828 | 0.3658 | | 0.0391 | 24.12 | 24000 | 0.5913 | 0.3685 | | 0.0312 | 24.62 | 24500 | 0.5850 | 0.3625 | | 0.0316 | 25.13 | 25000 | 0.6029 | 0.3611 | | 0.0282 | 25.63 | 25500 | 0.6312 | 0.3624 | | 0.0328 | 26.13 | 26000 | 0.6312 | 0.3621 | | 0.0258 | 26.63 | 26500 | 0.5891 | 0.3581 | | 0.0256 | 27.14 | 27000 | 0.6259 | 0.3546 | | 0.0255 | 27.64 | 27500 | 0.6315 | 0.3587 | | 0.0249 | 28.14 | 28000 | 0.6547 | 0.3579 | | 0.025 | 28.64 | 28500 | 0.6237 | 0.3565 | | 0.0228 | 29.15 | 29000 | 0.6187 | 0.3559 | | 0.0209 | 29.65 | 29500 | 0.6259 | 0.3544 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}

202015004/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

{}

202015004/wav2vec2-base-timit-trial_by_SHREYA

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

{}

275Gameplay/test

2022-03-02T23:29:04+00:00

text-generation

transformers

# Deadpool DialoGPT Model

{"tags": ["conversational"]}

2early4coffee/DialoGPT-medium-deadpool

2022-03-02T23:29:04+00:00

text-generation

transformers

# Deadpool DialoGPT Model

{"tags": ["conversational"]}

2early4coffee/DialoGPT-small-deadpool

2022-03-02T23:29:04+00:00

{}

2umm3r/bert-base-uncased-finetuned-cls

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7816 - Matthews Correlation: 0.5156 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5291 | 1.0 | 535 | 0.5027 | 0.4092 | | 0.3492 | 2.0 | 1070 | 0.5136 | 0.4939 | | 0.2416 | 3.0 | 1605 | 0.6390 | 0.5056 | | 0.1794 | 4.0 | 2140 | 0.7816 | 0.5156 | | 0.1302 | 5.0 | 2675 | 0.8836 | 0.5156 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5155709926752544, "name": "Matthews Correlation"}]}]}]}

2umm3r/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

feature-extraction

transformers

this is a fine tuned GPT2 text generation model on a Hunter x Hunter TV anime series dataset.\ you can find a link to the used dataset here : https://www.kaggle.com/bkoozy/hunter-x-hunter-subtitles you can find a colab notebook for fine-tuning the gpt2 model here : https://github.com/3koozy/fine-tune-gpt2-HxH/

{}

3koozy/gpt2-HxH

2022-03-02T23:29:04+00:00

{}

3zooze/Dd

2022-03-02T23:29:04+00:00

{}

Akshay-Vs/AI

2022-03-02T23:29:04+00:00

{}

511663/bert_finetuning_test

2022-03-02T23:29:04+00:00

{}

54Tor/test

2022-03-02T23:29:04+00:00

{}

5dimension/test

2022-03-02T23:29:04+00:00

{}

609ead0502/test

2022-03-02T23:29:04+00:00

{}

61birds/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

{}

842458199/model_name

2022-03-02T23:29:04+00:00

{}

850886470/xxy_gpt2_chinese

2022-03-02T23:29:04+00:00

{}

873101411/distilbert-base-uncased-finetuned-squad

2022-03-02T23:29:04+00:00

{}

91Rodman/111

2022-03-02T23:29:04+00:00

{}

923/distilbert-base-uncased-finetuned-squad

2022-03-02T23:29:04+00:00

token-classification

transformers

## Model description This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding noise with a either visually or phonologically resembled characters. Consequently, the fine-tuned model can achieve 96% accuracy on our test dataset. ## Intended uses & limitations You can use this model directly with a pipeline for token classification: ```python >>> from transformers import (AutoModelForTokenClassification, AutoTokenizer) >>> from transformers import pipeline >>> hub_model_id = "9pinus/macbert-base-chinese-medical-collation" >>> model = AutoModelForTokenClassification.from_pretrained(hub_model_id) >>> tokenizer = AutoTokenizer.from_pretrained(hub_model_id) >>> classifier = pipeline('ner', model=model, tokenizer=tokenizer) >>> result = classifier("如果病情较重，可适当口服甲肖唑片、环酯红霉素片等药物进行抗感染镇痛。") >>> for item in result: >>> if item['entity'] == 1: >>> print(item) {'entity': 1, 'score': 0.58127016, 'index': 14, 'word': '肖', 'start': 13, 'end': 14} ``` ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

{"language": "zh", "license": "apache-2.0", "tags": ["Token Classification"], "metrics": ["precision", "recall", "f1", "accuracy"]}

9pinus/macbert-base-chinese-medical-collation

2022-03-02T23:29:04+00:00

token-classification

transformers

## Model description This model is a fine-tuned version of bert-base-chinese for the purpose of medicine name recognition. We fine-tuned bert-base-chinese on a 500M dataset including 100K+ authorized medical articles on which we labeled all the medicine names. The model achieves 92% accuracy on our test dataset. ## Intended use ```python >>> from transformers import (AutoModelForTokenClassification, AutoTokenizer) >>> from transformers import pipeline >>> hub_model_id = "9pinus/macbert-base-chinese-medicine-recognition" >>> model = AutoModelForTokenClassification.from_pretrained(hub_model_id) >>> tokenizer = AutoTokenizer.from_pretrained(hub_model_id) >>> classifier = pipeline('ner', model=model, tokenizer=tokenizer) >>> result = classifier("如果病情较重，可适当口服甲硝唑片、环酯红霉素片、吲哚美辛片等药物进行抗感染镇痛。") >>> for item in result: >>> if item['entity'] == 1 or item['entity'] == 2: >>> print(item) {'entity': 1, 'score': 0.99999595, 'index': 13, 'word': '甲', 'start': 12, 'end': 13} {'entity': 2, 'score': 0.9999957, 'index': 14, 'word': '硝', 'start': 13, 'end': 14} {'entity': 2, 'score': 0.99999166, 'index': 15, 'word': '唑', 'start': 14, 'end': 15} {'entity': 2, 'score': 0.99898833, 'index': 16, 'word': '片', 'start': 15, 'end': 16} {'entity': 1, 'score': 0.9999864, 'index': 18, 'word': '环', 'start': 17, 'end': 18} {'entity': 2, 'score': 0.99999404, 'index': 19, 'word': '酯', 'start': 18, 'end': 19} {'entity': 2, 'score': 0.99999475, 'index': 20, 'word': '红', 'start': 19, 'end': 20} {'entity': 2, 'score': 0.9999964, 'index': 21, 'word': '霉', 'start': 20, 'end': 21} {'entity': 2, 'score': 0.9999951, 'index': 22, 'word': '素', 'start': 21, 'end': 22} {'entity': 2, 'score': 0.9990088, 'index': 23, 'word': '片', 'start': 22, 'end': 23} {'entity': 1, 'score': 0.9999975, 'index': 25, 'word': '吲', 'start': 24, 'end': 25} {'entity': 2, 'score': 0.9999957, 'index': 26, 'word': '哚', 'start': 25, 'end': 26} {'entity': 2, 'score': 0.9999945, 'index': 27, 'word': '美', 'start': 26, 'end': 27} {'entity': 2, 'score': 0.9999933, 'index': 28, 'word': '辛', 'start': 27, 'end': 28} {'entity': 2, 'score': 0.99949837, 'index': 29, 'word': '片', 'start': 28, 'end': 29} ``` ## Training and evaluation data ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

{"language": ["zh"], "license": "apache-2.0", "tags": ["Token Classification"]}

9pinus/macbert-base-chinese-medicine-recognition

2022-03-02T23:29:04+00:00

text-classification

transformers

bert-base-cased model trained on quora question pair dataset. The task requires to predict whether the two given sentences (or questions) are `not_duplicate` (label 0) or `duplicate` (label 1). The model achieves 89% evaluation accuracy

{"datasets": ["qqp"], "inference": false}

A-bhimany-u08/bert-base-cased-qqp

2022-03-02T23:29:04+00:00

{}

AAli/bert-base-cased-wikitext2

2022-03-02T23:29:04+00:00

{}

AAli/bert-base-uncased-finetuned-swag

2022-03-02T23:29:04+00:00

{}

AAli/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

{}

AAli/distilbert-base-uncased-finetuned-ner

2022-03-02T23:29:04+00:00

{}

AAli/distilbert-base-uncased-finetuned-squad

2022-03-02T23:29:04+00:00

{}

AAli/distilgpt2-finetuned-wikitext2

2022-03-02T23:29:04+00:00

{}

AAli/gpt2-wikitext2

2022-03-02T23:29:04+00:00

{}

AAli/my-new-shiny-tokenizer

2022-03-02T23:29:04+00:00

{}

AAli/opus-mt-en-ro-finetuned-en-to-ro

2022-03-02T23:29:04+00:00

{}

AAli/t5-small-finetuned-xsum

2022-03-02T23:29:04+00:00

{}

AAli/wav2vec2-base-demo-colab

2022-03-02T23:29:04+00:00

{}

AAli/wav2vec2-base-finetuned-ks

2022-03-02T23:29:04+00:00

text-generation

transformers

@Harry Potter DialoGPT model

{"tags": ["conversational"]}

ABBHISHEK/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

feature-extraction

transformers

Pre trained on clus_ chapter only.

{}

AG/pretraining

2022-03-02T23:29:04+00:00

{}

AHussain0418/distillbert-truth-detector

2022-03-02T23:29:04+00:00

{"license": "apache-2.0"}

AI-Ahmed/DisDistilBert-sst-N-Grams-en

2022-03-02T23:29:04+00:00

sentence-similarity

sentence-transformers

# PatentSBERTa ## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT ### Aalborg University Business School, AI: Growth-Lab https://arxiv.org/abs/2103.11933 https://github.com/AI-Growth-Lab/PatentSBERTa This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('AI-Growth-Lab/PatentSBERTa') 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 def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('AI-Growth-Lab/PatentSBERTa') model = AutoModel.from_pretrained('AI-Growth-Lab/PatentSBERTa') # 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, cls pooling. sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 5 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": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors ```LaTeX @article{bekamiri2021patentsberta, title={PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT}, author={Bekamiri, Hamid and Hain, Daniel S and Jurowetzki, Roman}, journal={arXiv preprint arXiv:2103.11933}, year={2021} } ```

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

AI-Growth-Lab/PatentSBERTa

2022-03-02T23:29:04+00:00

text2text-generation

transformers

# Model Trained Using AutoNLP - Problem type: Machine Translation - Model ID: 474612462 - CO2 Emissions (in grams): 133.0219882109991 ## Validation Metrics - Loss: 1.336498737335205 - Rouge1: 52.5404 - Rouge2: 31.6639 - RougeL: 50.1696 - RougeLsum: 50.3398 - Gen Len: 39.046 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/EricPeter/autonlp-EN-LUG-474612462 ```

{"language": "unk", "tags": "autonlp", "datasets": ["Eric Peter/autonlp-data-EN-LUG"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 133.0219882109991}

AI-Lab-Makerere/en_lg

2022-03-02T23:29:04+00:00

text2text-generation

transformers

# Model Trained Using AutoNLP - Problem type: Machine Translation - Model ID: 475112539 - CO2 Emissions (in grams): 126.34446293851818 ## Validation Metrics - Loss: 1.5376628637313843 - Rouge1: 62.4613 - Rouge2: 39.4759 - RougeL: 58.183 - RougeLsum: 58.226 - Gen Len: 26.5644 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/EricPeter/autonlp-MarianMT_lg_en-475112539 ```

{"language": "unk", "tags": "autonlp", "datasets": ["EricPeter/autonlp-data-MarianMT_lg_en"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 126.34446293851818}

AI-Lab-Makerere/lg_en

2022-03-02T23:29:04+00:00

fill-mask

transformers

# A Swedish Bert model ## Model description This model follows the Bert Large model architecture as implemented in [Megatron-LM framework](https://github.com/NVIDIA/Megatron-LM). It was trained with a batch size of 512 in 600k steps. The model contains following parameters: <figure> | Hyperparameter | Value | |----------------------|------------| | \\(n_{parameters}\\) | 340M | | \\(n_{layers}\\) | 24 | | \\(n_{heads}\\) | 16 | | \\(n_{ctx}\\) | 1024 | | \\(n_{vocab}\\) | 30592 | ## Training data The model is pretrained on a Swedish text corpus of around 85 GB from a variety of sources as shown below. <figure> | Dataset | Genre | Size(GB)| |----------------------|------|------| | Anföranden | Politics |0.9| |DCEP|Politics|0.6| |DGT|Politics|0.7| |Fass|Medical|0.6| |Författningar|Legal|0.1| |Web data|Misc|45.0| |JRC|Legal|0.4| |Litteraturbanken|Books|0.3O| |SCAR|Misc|28.0| |SOU|Politics|5.3| |Subtitles|Drama|1.3| |Wikipedia|Facts|1.8| ## Intended uses & limitations The raw model can be used for the usual tasks of masked language modeling or next sentence prediction. It is also often fine-tuned on a downstream task to improve its performance in a specific domain/task. <br> <br> ## How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI-Nordics/bert-large-swedish-cased") model = AutoModelForMaskedLM.from_pretrained("AI-Nordics/bert-large-swedish-cased")

{"language": "sv"}

AI-Nordics/bert-large-swedish-cased

2022-03-02T23:29:04+00:00

{}

IssakaAI/wav2vec2-large-xls-r-300m-turkish-colab

2022-03-02T23:29:04+00:00

token-classification

transformers

{"license": "mit"}

AI4Sec/cyner-xlm-roberta-base

2022-03-02T23:29:04+00:00

token-classification

transformers

{"license": "mit"}

AI4Sec/cyner-xlm-roberta-large

2022-03-02T23:29:04+00:00

sentence-similarity

sentence-transformers

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1438 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 2, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 288, "weight_decay": 0.05 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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 -->

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

AIDA-UPM/MSTSb_paraphrase-multilingual-MiniLM-L12-v2

2022-03-02T23:29:04+00:00

sentence-similarity

sentence-transformers

# AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1438 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 2, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 4e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 288, "weight_decay": 0.1 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1

2022-03-02T23:29:04+00:00

sentence-similarity

sentence-transformers

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1438 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 4e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

AIDA-UPM/MSTSb_stsb-xlm-r-multilingual

2022-03-02T23:29:04+00:00

text-classification

transformers

# bertweet-base-multi-mami This is a Bertweet model: It maps sentences & paragraphs to a 768 dimensional dense vector space and classifies them into 5 multi labels. # Multilabels label2id={ "misogynous": 0, "shaming": 1, "stereotype": 2, "objectification": 3, "violence": 4, },

{"language": "en", "license": "apache-2.0", "tags": ["text-classification", "misogyny"], "pipeline_tag": "text-classification", "widget": [{"text": "Women wear yoga pants because men don't stare at their personality", "example_title": "Misogyny detection"}]}

AIDA-UPM/bertweet-base-multi-mami

2022-03-02T23:29:04+00:00

sentence-similarity

transformers

# mstsb-paraphrase-multilingual-mpnet-base-v2 This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences. <!--- Describe your model here --> This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below). <!-- ## 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 # It support several languages sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"] # The pooling technique is automatically detected (mean pooling) model = SentenceTransformer('mstsb-paraphrase-multilingual-mpnet-base-v2') 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 # We should define the proper pooling function: Mean pooling # 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 = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2') model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es). Here we compare the average multilingual semantic textual similairty capabilities between the `paraphrase-multilingual-mpnet-base-v2` based model and the `mstsb-paraphrase-multilingual-mpnet-base-v2` fine-tuned model across the 31 tasks. It is worth noting that both models are multilingual, but the second model is adjusted with multilingual data for semantic similarity. The average of correlation coefficients is computed by transforming each correlation coefficient to a Fisher's z value, averaging them, and then back-transforming to a correlation coefficient. | Model | Average Spearman Cosine Test | |:---------------------------------------------:|:------------------------------:| | mstsb-paraphrase-multilingual-mpnet-base-v2 | 0.835890 | | paraphrase-multilingual-mpnet-base-v2 | 0.818896 | <br> The following tables breakdown the performance of `mstsb-paraphrase-multilingual-mpnet-base-v2` according to the different tasks. For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks. | Monolingual Task | Pearson Cosine test | Spearman Cosine test | |:------------------:|:---------------------:|:-----------------------:| | en;en | 0.868048310692506 | 0.8740170943535747 | | ar;ar | 0.8267139454193487 | 0.8284459741532022 | | cs;cs | 0.8466821720942157 | 0.8485417688803879 | | de;de | 0.8517285961812183 | 0.8557680051557893 | | es;es | 0.8519185309064691 | 0.8552243211580456 | | fr;fr | 0.8430951067985064 | 0.8466614534379704 | | hi;hi | 0.8178258630578092 | 0.8176462079184331 | | it;it | 0.8475909574305637 | 0.8494216064459076 | | ja;ja | 0.8435588859386477 | 0.8456031494178619 | | nl;nl | 0.8486765104527032 | 0.8520856765262531 | | pl;pl | 0.8407840177883407 | 0.8443070467300299 | | pt;pt | 0.8534880178249296 | 0.8578544068829622 | | ru;ru | 0.8390897585455678 | 0.8423041443534423 | | tr;tr | 0.8382125451820572 | 0.8421587450058385 | | zh-CN;zh-CN | 0.826233678946644 | 0.8248515460782744 | | zh-TW;zh-TW | 0.8242683809675422 | 0.8235506799952028 | <br> | Cross-lingual Task | Pearson Cosine test | Spearman Cosine test | |:--------------------:|:---------------------:|:-----------------------:| | en;ar | 0.7990830340462535 | 0.7956792016468148 | | en;cs | 0.8381274879061265 | 0.8388713450024455 | | en;de | 0.8414439600928739 | 0.8441971698649943 | | en;es | 0.8442337511356952 | 0.8445035292903559 | | en;fr | 0.8378437644605063 | 0.8387903367907733 | | en;hi | 0.7951955086055527 | 0.7905052217683244 | | en;it | 0.8415686372978766 | 0.8419480899107785 | | en;ja | 0.8094306665283388 | 0.8032512280936449 | | en;nl | 0.8389526140129767 | 0.8409310421803277 | | en;pl | 0.8261309163979578 | 0.825976253023656 | | en;pt | 0.8475546209070765 | 0.8506606391790897 | | en;ru | 0.8248514914263723 | 0.8224871183202255 | | en;tr | 0.8191803661207868 | 0.8194200775744044 | | en;zh-CN | 0.8147678083378249 | 0.8102089470690433 | | en;zh-TW | 0.8107272160374955 | 0.8056129680510944 | ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 687 with parameters: ``` {'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 2, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 140, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->

{"language": "multilingual", "tags": ["feature-extraction", "sentence-similarity", "transformers", "multilingual"], "pipeline_tag": "sentence-similarity"}

AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2

2022-03-02T23:29:04+00:00

text-classification

transformers

This is a finetuned XLM-RoBERTA model for natural language inference. It has been trained with a massive ammount of data following the ANLI pipeline training. We include data from: - [mnli](https://cims.nyu.edu/~sbowman/multinli/) {train, dev and test} - [snli](https://nlp.stanford.edu/projects/snli/) {train, dev and test} - [xnli](https://github.com/facebookresearch/XNLI) {train, dev and test} - [fever](https://fever.ai/resources.html) {train, dev and test} - [anli](https://github.com/facebookresearch/anli) {train} The model is validated on ANLI training sets, including R1, R2 and R3. The following results can be expected on the testing splits. | Split | Accuracy | - | - | | R1 | 0.6610 | R2 | 0.4990 | R3 | 0.4425 # Multilabels label2id={ "contradiction": 0, "entailment": 1, "neutral": 2, },

{"language": "en", "license": "apache-2.0", "tags": ["natural-language-inference", "misogyny"], "pipeline_tag": "text-classification", "widget": [{"text": "Las mascarillas causan hipoxia. Wearing masks is harmful to human health", "example_title": "Natural Language Inference"}]}

AIDA-UPM/xlm-roberta-large-snli_mnli_xnli_fever_r1_r2_r3

2022-03-02T23:29:04+00:00

text-generation

transformers

# tests

{"tags": ["conversational"]}

AIDynamics/DialoGPT-medium-MentorDealerGuy

2022-03-02T23:29:04+00:00

text-generation

transformers

# Uses DialoGPT

{"tags": ["conversational"]}

AJ/DialoGPT-small-ricksanchez

2022-03-02T23:29:04+00:00

{}

AJ/rick-ai

2022-03-02T23:29:04+00:00

{}

AJ/rick-bot

2022-03-02T23:29:04+00:00

text-generation

transformers

# its rick from rick and morty

{"tags": ["conversational", "humor"]}

AJ/rick-discord-bot

2022-03-02T23:29:04+00:00

text-generation

# uses dialogpt

{"tags": ["conversational", "funny"]}

AJ/rick-sanchez-bot

2022-03-02T23:29:04+00:00

text-generation

transformers

# Harry Potter DialoGPT model

{"tags": ["conversational"]}

AJ-Dude/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

fill-mask

transformers

{}

AK/ak_nlp

2022-03-02T23:29:04+00:00

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

AK270802/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

{}

AKMyscich/VetTrain-v1.2

2022-03-02T23:29:04+00:00

{}

AKulk/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-base-timit-epochs10 This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs5](https://huggingface.co/AKulk/wav2vec2-base-timit-epochs5) on the None 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs10", "results": []}]}

AKulk/wav2vec2-base-timit-epochs10

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-base-timit-epochs15 This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs10](https://huggingface.co/AKulk/wav2vec2-base-timit-epochs10) on the None 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs15", "results": []}]}

AKulk/wav2vec2-base-timit-epochs15

2022-03-02T23:29:04+00:00

{}

AKulk/wav2vec2-base-timit-epochs20

2022-03-02T23:29:04+00:00

automatic-speech-recognition

transformers

<!-- 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. --> # wav2vec2-base-timit-epochs5 This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the None 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-epochs5", "results": []}]}

AKulk/wav2vec2-base-timit-epochs5

2022-03-02T23:29:04+00:00

fill-mask

transformers

{}

ALINEAR/albert-japanese-v2

2022-03-02T23:29:04+00:00

fill-mask

transformers

{}

ALINEAR/albert-japanese

2022-03-02T23:29:04+00:00

{}

ALaks96/distilbart-cnn-12-6

2022-03-02T23:29:04+00:00

{}

ARATHI/electra-small-discriminator-fintuned-cola

2022-03-02T23:29:04+00:00

{}

ARCYVILK/gpt2-bot

2022-03-02T23:29:04+00:00

summarization

transformers

# summarization_fanpage128 This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.5348 - Rouge1: 34.1882 - Rouge2: 15.7866 - Rougel: 25.141 - Rougelsum: 28.4882 - Gen Len: 69.3041 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage-128") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-fanpage-128") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_fanpage128", "results": []}]}

ARTeLab/it5-summarization-fanpage

2022-03-02T23:29:04+00:00

summarization

transformers

# summarization_ilpost This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 1.6020 - Rouge1: 33.7802 - Rouge2: 16.2953 - Rougel: 27.4797 - Rougelsum: 30.2273 - Gen Len: 45.3175 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_ilpost", "results": []}]}

ARTeLab/it5-summarization-ilpost

2022-03-02T23:29:04+00:00

summarization

transformers

# summarization_mlsum This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on MLSum-it for Abstractive Summarization. It achieves the following results: - Loss: 2.0190 - Rouge1: 19.3739 - Rouge2: 5.9753 - Rougel: 16.691 - Rougelsum: 16.7862 - Gen Len: 32.5268 ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum") model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-mlsum") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "gsarti/it5-base", "model-index": [{"name": "summarization_mlsum", "results": []}]}

ARTeLab/it5-summarization-mlsum

2022-03-02T23:29:04+00:00

summarization

transformers

# mbart-summarization-fanpage This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on Fanpage dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.1833 - Rouge1: 36.5027 - Rouge2: 17.4428 - Rougel: 26.1734 - Rougelsum: 30.2636 - Gen Len: 75.2413 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/fanpage"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_fanpage4epoch", "results": []}]}

ARTeLab/mbart-summarization-fanpage

2022-03-02T23:29:04+00:00

summarization

transformers

# mbart_summarization_ilpost This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on IlPost dataset for Abstractive Summarization. It achieves the following results: - Loss: 2.3640 - Rouge1: 38.9101 - Rouge2: 21.384 - Rougel: 32.0517 - Rougelsum: 35.0743 - Gen Len: 39.8843 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-ilpost") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-ilpost") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/ilpost"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_ilpost", "results": []}]}

ARTeLab/mbart-summarization-ilpost

2022-03-02T23:29:04+00:00

summarization

transformers

# mbart_summarization_mlsum This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization. It achieves the following results: - Loss: 3.3336 - Rouge1: 19.3489 - Rouge2: 6.4028 - Rougel: 16.3497 - Rougelsum: 16.5387 - Gen Len: 33.5945 ## Usage ```python from transformers import MBartTokenizer, MBartForConditionalGeneration tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum") model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-mlsum") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3 # Citation More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

{"language": ["it"], "tags": ["summarization"], "datasets": ["ARTeLab/mlsum-it"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-cc25", "model-index": [{"name": "summarization_mbart_mlsum", "results": []}]}

ARTeLab/mbart-summarization-mlsum

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- 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. --> # PENGMENGJIE-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "PENGMENGJIE-finetuned-emotion", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]}

ASCCCCCCCC/PENGMENGJIE-finetuned-emotion

2022-03-02T23:29:04+00:00

{"license": "apache-2.0"}

ASCCCCCCCC/PENGMENGJIE

2022-03-02T23:29:04+00:00

{}

ASCCCCCCCC/PMJ

2022-03-02T23:29:04+00:00

text-classification

transformers

{}

ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- 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-chinese-finetuned-amazon_zh_20000 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1683 - Accuracy: 0.5224 - F1: 0.5194 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.2051 | 1.0 | 2500 | 1.1717 | 0.506 | 0.4847 | | 1.0035 | 2.0 | 5000 | 1.1683 | 0.5224 | 0.5194 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-chinese-finetuned-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- 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-chinese-amazon_zh_20000 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1518 - Accuracy: 0.5092 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.196 | 1.0 | 1250 | 1.1518 | 0.5092 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-chinese-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- 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-multilingual-cased-amazon_zh_20000 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3031 - Accuracy: 0.4406 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.396 | 1.0 | 1250 | 1.3031 | 0.4406 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-multilingual-cased-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-amazon_zh_20000 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3516 - Accuracy: 0.414 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4343 | 1.0 | 1250 | 1.3516 | 0.414 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon_zh_20000", "results": []}]}

ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000

2022-03-02T23:29:04+00:00

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.9.0 - Pytorch 1.7.1+cpu - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}}]}]}

ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc

2022-03-02T23:29:04+00:00

{}

AT/bert-base-uncased-finetuned-wikitext2

2022-03-02T23:29:04+00:00

{}

AT/distilbert-base-cased-finetuned-wikitext2

2022-03-02T23:29:04+00:00

{}

AT/distilgpt2-finetuned-wikitext2

2022-03-02T23:29:04+00:00

fill-mask

transformers

<!-- 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None 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: 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: 80.0 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]}

AT/distilroberta-base-finetuned-wikitext2

2022-03-02T23:29:04+00:00

text-generation

transformers

#Harry Potter DialoGPT Model

{"tags": ["conversational"]}

ATGdev/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

{}

ATGdev/ai_ironman

2022-03-02T23:29:04+00:00

{"license": "cc-by-nc-4.0"}

AUBMC-AIM/MammoGANesis

2022-03-02T23:29:04+00:00

{"license": "cc-by-nc-4.0"}

AUBMC-AIM/OCTaGAN

2022-03-02T23:29:04+00:00

{}

AVAIYA/python-test

2022-03-02T23:29:04+00:00

fill-mask

transformers

<!-- 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. --> # result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7458 ## 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "result", "results": []}]}

AVSilva/bertimbau-large-fine-tuned-md

2022-03-02T23:29:04+00:00

fill-mask

transformers

<!-- 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. --> # result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7570 ## 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "result", "results": []}]}

AVSilva/bertimbau-large-fine-tuned-sd

2022-03-02T23:29:04+00:00

text-generation

transformers

#Tony Stark DialoGPT model

{"tags": ["conversational"]}

AVeryRealHuman/DialoGPT-small-TonyStark

2022-03-02T23:29:04+00:00