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neal49/distilbert-yelp
2c01f4f76b10b9a3205f1cc72367ac8afa412555
2022-05-08T07:58:16.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
neal49
null
neal49/distilbert-yelp
7
null
transformers
14,400
Entry not found
sam999/distilbert-base-uncased-finetuned-squad
d36279c86ccbda4497c02661aa40cffc3ca23f5d
2022-05-08T18:17:42.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
sam999
null
sam999/distilbert-base-uncased-finetuned-squad
7
null
transformers
14,401
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6908 ## 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: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8853 | 0.2 | 1107 | 1.6908 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
binay1999/bert-finetuned-text-classification
e53b38f1b1af994edc2c72916e29700735047033
2022-05-09T13:14:07.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
binay1999
null
binay1999/bert-finetuned-text-classification
7
null
transformers
14,402
Entry not found
domischwimmbeck/bert-base-german-cased-own-data-ner
2d52f993106a7429743174462a4773b0883dcc64
2022-05-20T09:38:35.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
domischwimmbeck
null
domischwimmbeck/bert-base-german-cased-own-data-ner
7
null
transformers
14,403
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-own-data-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-own-data-ner This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0535 - Precision: 0.7134 - Recall: 0.8536 - F1: 0.7772 - Accuracy: 0.9895 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.8 | 32 | 0.0308 | 0.7593 | 0.8 | 0.7791 | 0.9917 | | No log | 1.6 | 64 | 0.0342 | 0.7756 | 0.8393 | 0.8062 | 0.9911 | | No log | 2.4 | 96 | 0.0457 | 0.7764 | 0.8679 | 0.8196 | 0.9906 | | No log | 3.2 | 128 | 0.0383 | 0.7524 | 0.8464 | 0.7966 | 0.9911 | | No log | 4.0 | 160 | 0.0420 | 0.7539 | 0.8536 | 0.8007 | 0.9907 | | No log | 4.8 | 192 | 0.0535 | 0.7134 | 0.8536 | 0.7772 | 0.9895 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
binay1999/ditilbert-finetuned-text-classification
414a7b737e3b5ff9f00802c342a235d5ab4fb0b9
2022-05-10T05:53:09.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
binay1999
null
binay1999/ditilbert-finetuned-text-classification
7
null
transformers
14,404
Entry not found
pglauner/distilbert-base-uncased-finetuned-emotion
389aab1a989739d086a334d97465cc9a3583c25d
2022-05-10T17:42:18.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
pglauner
null
pglauner/distilbert-base-uncased-finetuned-emotion
7
null
transformers
14,405
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9265216393152228 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2251 - Accuracy: 0.9265 - F1: 0.9265 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8432 | 1.0 | 250 | 0.3353 | 0.8975 | 0.8939 | | 0.2582 | 2.0 | 500 | 0.2251 | 0.9265 | 0.9265 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
guhuawuli/distilbert-poem_key_words
02bbcba61fda1c4669edb77ca1c98ee1d2c04442
2022-05-12T01:55:46.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
guhuawuli
null
guhuawuli/distilbert-poem_key_words
7
null
transformers
14,406
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-poem_key_words results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-poem_key_words This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 338 | 0.2103 | 0.9378 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0a0+3fd9dcf - Datasets 2.1.0 - Tokenizers 0.12.1
bookbot/wav2vec2-adult-child-id-cls
3198c3ae5b34979dba4d07e0077eeae31a5f12bc
2022-05-12T12:36:55.000Z
[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "id", "arxiv:2006.11477", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
audio-classification
false
bookbot
null
bookbot/wav2vec2-adult-child-id-cls
7
null
transformers
14,407
--- language: id license: apache-2.0 tags: - audio-classification - generated_from_trainer metrics: - accuracy - f1 model-index: - name: wav2vec2-adult-child-id-cls results: [] --- # Wav2Vec2 Adult/Child Indonesian Speech Classifier Wav2Vec2 Adult/Child Indonesian Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a fine-tuned version of [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on a private adult/child Indonesian speech classification dataset. This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard. ## Model | Model | #params | Arch. | Training/Validation data (text) | | ----------------------------- | ------- | ----------- | ---------------------------------------------------- | | `wav2vec2-adult-child-id-cls` | 91M | wav2vec 2.0 | Adult/Child Indonesian Speech Classification Dataset | ## Evaluation Results The model achieves the following results on evaluation: | Dataset | Loss | Accuracy | F1 | | -------------------------------------------- | ------ | -------- | ------ | | Adult/Child Indonesian Speech Classification | 0.2603 | 92.22% | 0.9202 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - `learning_rate`: 3e-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 - `lr_scheduler_warmup_ratio`: 0.1 - `gradient_accumulation_steps`: 1 - `num_epochs`: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | :-----------: | :---: | :--: | :-------------: | :------: | :----: | | 0.2415 | 1.0 | 305 | 0.2951 | 0.8804 | 0.8695 | | 0.202 | 2.0 | 610 | 0.2392 | 0.9124 | 0.9081 | | 0.2161 | 3.0 | 915 | 0.2508 | 0.9199 | 0.9161 | | 0.1348 | 4.0 | 1220 | 0.2748 | 0.9153 | 0.9126 | | 0.162 | 5.0 | 1525 | 0.2603 | 0.9222 | 0.9202 | ## Disclaimer Do consider the biases which came from pre-training datasets that may be carried over into the results of this model. ## Authors Wav2Vec2 Adult/Child Indonesian Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle. ## Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.2.0 - Tokenizers 0.12.1
nielsr/pix2seq-simple
40d35145fee7cd254502e797e3b2982eb8b44743
2022-05-11T10:07:50.000Z
[ "pytorch", "pix2seq", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
nielsr
null
nielsr/pix2seq-simple
7
null
transformers
14,408
Entry not found
ceggian/sbert_pt_reddit_mnr_256
5826be53a7bd0fe43daa5de5d340c6c27a21a26b
2022-05-11T18:03:58.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
ceggian
null
ceggian/sbert_pt_reddit_mnr_256
7
null
sentence-transformers
14,409
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> 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**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39289 with parameters: ``` {'batch_size': 8} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` 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": 3928, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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 -->
enoriega/kw_pubmed_5000_0.0003
e5cca2e7635dc36ebfb260eec4a5db02777b322e
2022-05-12T09:02:44.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
enoriega
null
enoriega/kw_pubmed_5000_0.0003
7
null
transformers
14,410
Entry not found
anuragshas/wav2vec2-xls-r-300m-or-cv9-with-lm
cdb050e67edc37171819980e39e6ce9639e6ba83
2022-05-17T22:40:52.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "or", "dataset:mozilla-foundation/common_voice_9_0", "transformers", "mozilla-foundation/common_voice_9_0", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
anuragshas
null
anuragshas/wav2vec2-xls-r-300m-or-cv9-with-lm
7
null
transformers
14,411
--- language: - or license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_9_0 - generated_from_trainer datasets: - mozilla-foundation/common_voice_9_0 metrics: - wer model-index: - name: XLS-R-300M - Odia results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: mozilla-foundation/common_voice_9_0 name: Common Voice 9 args: or metrics: - type: wer value: 44.343 name: Test WER - name: Test CER type: cer value: 10.989 --- <!-- 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - OR dataset. It achieves the following results on the evaluation set: - Loss: 0.7886 - Wer: 0.5495 - Cer: 0.1311 ## 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: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3071 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 3.5875 | 66.62 | 400 | 3.4289 | 1.0 | 1.0 | | 1.4065 | 133.31 | 800 | 0.7243 | 0.6619 | 0.1734 | | 1.007 | 199.92 | 1200 | 0.6611 | 0.5831 | 0.1457 | | 0.7984 | 266.62 | 1600 | 0.6387 | 0.5520 | 0.1332 | | 0.6117 | 333.31 | 2000 | 0.7424 | 0.5682 | 0.1376 | | 0.4926 | 399.92 | 2400 | 0.7627 | 0.5514 | 0.1314 | | 0.416 | 466.62 | 2800 | 0.7816 | 0.5604 | 0.1320 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.1.1.dev0 - Tokenizers 0.12.1
gary109/STAS_yolos-small
6c03e7922f73eacc392669bb544006b074c669ab
2022-05-12T14:48:32.000Z
[ "pytorch", "yolos", "object-detection", "transformers" ]
object-detection
false
gary109
null
gary109/STAS_yolos-small
7
null
transformers
14,412
Entry not found
ali-issa/2-wav2vec2-arabic-gpu-colab-similar-to-german
223888abd04749ad87da00c4b279c09a227ed067
2022-05-14T10:05:04.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
ali-issa
null
ali-issa/2-wav2vec2-arabic-gpu-colab-similar-to-german
7
null
transformers
14,413
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-arabic-gpu-colab-similar-to-german 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. --> # wav2vec2-arabic-gpu-colab-similar-to-german This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6127 - Wer: 0.4322 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.4731 | 2.83 | 400 | 2.9055 | 1.0 | | 2.0367 | 5.67 | 800 | 0.7727 | 0.6700 | | 0.8081 | 8.51 | 1200 | 0.6407 | 0.5320 | | 0.5753 | 11.35 | 1600 | 0.5982 | 0.4709 | | 0.4604 | 14.18 | 2000 | 0.5999 | 0.4651 | | 0.3902 | 17.02 | 2400 | 0.6001 | 0.4469 | | 0.3443 | 19.85 | 2800 | 0.5957 | 0.4404 | | 0.3152 | 22.69 | 3200 | 0.5911 | 0.4304 | | 0.2924 | 25.53 | 3600 | 0.6170 | 0.4392 | | 0.2779 | 28.37 | 4000 | 0.6127 | 0.4322 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
ahmeddbahaa/mbart-large-50-finetuned-persian
0f8a1bca38e3dfe752c9cceaaaf3718bd5f998c8
2022-05-15T04:01:56.000Z
[ "pytorch", "mbart", "text2text-generation", "dataset:xlsum", "transformers", "summarization", "persian", "MBart50", "Abstractive Summarization", "generated_from_trainer", "model-index", "autotrain_compatible" ]
summarization
false
ahmeddbahaa
null
ahmeddbahaa/mbart-large-50-finetuned-persian
7
null
transformers
14,414
--- tags: - summarization - persian - MBart50 - Abstractive Summarization - generated_from_trainer datasets: - xlsum model-index: - name: mbart-large-50-finetuned-persian 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. --> # mbart-large-50-finetuned-persian This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 4.1932 - Rouge-1: 26.11 - Rouge-2: 8.11 - Rouge-l: 21.09 - Gen Len: 37.29 - Bertscore: 71.08 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 5.5612 | 1.0 | 1476 | 4.5015 | 17.07 | 3.14 | 13.54 | 47.49 | 66.83 | | 4.3049 | 2.0 | 2952 | 4.1055 | 22.63 | 5.89 | 18.03 | 40.43 | 69.23 | | 3.8154 | 3.0 | 4428 | 3.9822 | 24.57 | 7.15 | 19.74 | 37.35 | 70.36 | | 3.3401 | 4.0 | 5904 | 4.0088 | 25.84 | 7.96 | 20.95 | 37.56 | 70.83 | | 2.8879 | 5.0 | 7380 | 4.1932 | 26.24 | 8.26 | 21.23 | 37.78 | 71.05 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
Jeevesh8/6ep_bert_ft_cola-73
1a8b525b3e0a7a69e7671513fdad9ae912f03a04
2022-05-14T14:00:39.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/6ep_bert_ft_cola-73
7
null
transformers
14,415
Entry not found
VictorZhu/Anchor-Classification-DMV
be337e323a32eb839e9c884c6b613afc9ef7e3ea
2022-06-08T15:58:40.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
VictorZhu
null
VictorZhu/Anchor-Classification-DMV
7
null
transformers
14,416
Entry not found
bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-00
8e04582343c227c3d217976d8a5b4ab8b74e53f5
2022-06-16T13:31:00.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
bekirbakar
null
bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-00
7
null
transformers
14,417
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-tr-fine-tuning-00 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. --> # wav2vec2-large-xlsr-53-tr-fine-tuning-00 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3974 - Wer: 0.4784 ## Training Procedure ### Training Hyper-parameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training Results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0376 | 4.21 | 400 | 2.4295 | 1.0 | | 0.8375 | 8.42 | 800 | 0.4717 | 0.6291 | | 0.3246 | 12.63 | 1200 | 0.4066 | 0.5528 | | 0.216 | 16.84 | 1600 | 0.4022 | 0.5149 | | 0.1664 | 21.05 | 2000 | 0.3972 | 0.5013 | | 0.1413 | 25.26 | 2400 | 0.3982 | 0.4894 | | 0.1197 | 29.47 | 2800 | 0.3974 | 0.4784 | ### Framework Versions - Transformers 4.19.1 - Pytorch 1.11.0+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
chrisvinsen/xlsr-wav2vec2-final-2
fd692f8a8794aff8c88e8eac1411e182efe5b7eb
2022-05-19T00:11:27.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
chrisvinsen
null
chrisvinsen/xlsr-wav2vec2-final-2
7
null
transformers
14,418
Entry not found
ankitkupadhyay/mt5-small-finetuned-multilingual-xlsum
d3b793b04d69bfc77561ed842a6ab5bb9f3d66d1
2022-05-16T22:44:17.000Z
[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "multilingual model", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
ankitkupadhyay
null
ankitkupadhyay/mt5-small-finetuned-multilingual-xlsum
7
null
transformers
14,419
--- license: apache-2.0 tags: - multilingual model - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-multilingual-xlsum 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. --> # mt5-small-finetuned-multilingual-xlsum This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7979 - Rouge1: 9.2017 - Rouge2: 2.3976 - Rougel: 7.7055 - Rougelsum: 7.7347 ## 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: 5.6e-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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 4.4524 | 1.0 | 3375 | 2.9251 | 8.1565 | 1.9058 | 6.7949 | 6.8196 | | 3.6707 | 2.0 | 6750 | 2.8524 | 8.7884 | 2.147 | 7.339 | 7.3678 | | 3.5273 | 3.0 | 10125 | 2.8184 | 9.1157 | 2.3886 | 7.6228 | 7.6592 | | 3.4452 | 4.0 | 13500 | 2.8028 | 9.2619 | 2.406 | 7.7607 | 7.7921 | | 3.4074 | 5.0 | 16875 | 2.7979 | 9.2017 | 2.3976 | 7.7055 | 7.7347 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
huawei-noah/AutoTinyBERT-S4
f7b81320c31659f1ad8213b92e182d7c181d7906
2022-05-16T14:57:40.000Z
[ "pytorch", "transformers", "license:other" ]
null
false
huawei-noah
null
huawei-noah/AutoTinyBERT-S4
7
null
transformers
14,420
--- license: other --- Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base). AutoTinyBERT provides a model zoo that can meet different latency requirements.
Amalq/autotrain-smm4h_large_roberta_clean-874027878
2be057f967885a1e04b37a466ee374703c8d720c
2022-05-16T18:44:14.000Z
[ "pytorch", "roberta", "text-classification", "unk", "dataset:Amalq/autotrain-data-smm4h_large_roberta_clean", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
Amalq
null
Amalq/autotrain-smm4h_large_roberta_clean-874027878
7
null
transformers
14,421
--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - Amalq/autotrain-data-smm4h_large_roberta_clean co2_eq_emissions: 9.123490454955585 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 874027878 - CO2 Emissions (in grams): 9.123490454955585 ## Validation Metrics - Loss: 0.35724225640296936 - Accuracy: 0.8571428571428571 - Precision: 0.7637362637362637 - Recall: 0.8910256410256411 - AUC: 0.9267555361305361 - F1: 0.8224852071005917 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Amalq/autotrain-smm4h_large_roberta_clean-874027878 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Suhong/distilbert-base-uncased-emotion-climateChange
0fc5939ef7e7f23d8c1e3d0cd916e1116d083f3a
2022-05-19T01:19:15.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Suhong
null
Suhong/distilbert-base-uncased-emotion-climateChange
7
null
transformers
14,422
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-emotion-climateChange results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-emotion-climateChange This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7189 - Accuracy: 0.8416 - F1: 0.7735 ## 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: 256 - eval_batch_size: 256 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 23 | 0.9234 | 0.8416 | 0.7735 | | No log | 2.0 | 46 | 0.7189 | 0.8416 | 0.7735 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
alk/pegasus-cnn-dailymail
ca147188f73b22343e95d014b76d94b6d0e69620
2022-05-17T05:36:34.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "dataset:cnn_dailymail", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
alk
null
alk/pegasus-cnn-dailymail
7
null
transformers
14,423
--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: pegasus-cnn-dailymail 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. --> # pegasus-cnn-dailymail This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.4497 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5344 | 0.6 | 500 | 1.4497 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
anuj55/all-MiniLM-L6-v2-finetuned-polifact
4bca151bef856d77b89d65e1c8944eeb4b93ce66
2022-05-17T12:28:07.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
anuj55
null
anuj55/all-MiniLM-L6-v2-finetuned-polifact
7
null
transformers
14,424
Entry not found
AnonymousSub/longformer-base-4096_squad2.0
7a4ab27dcf55e80d91d211aa43e5ebc989e0e5bb
2022-05-18T23:50:21.000Z
[ "pytorch", "longformer", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
AnonymousSub
null
AnonymousSub/longformer-base-4096_squad2.0
7
null
transformers
14,425
Entry not found
leonweber/bunsen_base_best
b070da5af192ce39140ace1c2c3b1f91b97087ce
2022-05-28T09:48:12.000Z
[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
leonweber
null
leonweber/bunsen_base_best
7
null
transformers
14,426
Entry not found
ankitkupadhyay/outputs
90cfb3b8565fa3c6b018bcff8871d1467deb1352
2022-05-19T19:13:39.000Z
[ "pytorch", "deberta-v2", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
ankitkupadhyay
null
ankitkupadhyay/outputs
7
null
transformers
14,427
--- license: mit tags: - generated_from_trainer model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0224 - Pearson: 0.8314 ## 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: 8e-05 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.0 | 214 | 0.0256 | 0.7816 | | No log | 2.0 | 428 | 0.0251 | 0.8115 | | 0.0355 | 3.0 | 642 | 0.0257 | 0.8186 | | 0.0355 | 4.0 | 856 | 0.0220 | 0.8255 | | 0.0133 | 5.0 | 1070 | 0.0226 | 0.8287 | | 0.0133 | 6.0 | 1284 | 0.0220 | 0.8321 | | 0.0133 | 7.0 | 1498 | 0.0224 | 0.8314 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
domischwimmbeck/bert-base-german-cased-20000-ner
144f21480096eb8d4cd67104dc21d43d3b042c45
2022-05-20T13:29:32.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
domischwimmbeck
null
domischwimmbeck/bert-base-german-cased-20000-ner
7
null
transformers
14,428
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-20000-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-20000-ner This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0826 - Precision: 0.8904 - Recall: 0.8693 - F1: 0.8797 - Accuracy: 0.9832 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.11 | 64 | 0.0840 | 0.8076 | 0.7842 | 0.7957 | 0.9752 | | No log | 0.23 | 128 | 0.0787 | 0.8119 | 0.7735 | 0.7922 | 0.9746 | | No log | 0.34 | 192 | 0.0677 | 0.8264 | 0.8362 | 0.8313 | 0.9794 | | No log | 0.45 | 256 | 0.0630 | 0.8440 | 0.8125 | 0.8280 | 0.9801 | | No log | 0.57 | 320 | 0.0664 | 0.8035 | 0.8391 | 0.8209 | 0.9782 | | No log | 0.68 | 384 | 0.0674 | 0.8850 | 0.8285 | 0.8558 | 0.9819 | | No log | 0.79 | 448 | 0.0631 | 0.8834 | 0.8598 | 0.8714 | 0.9825 | | 0.094 | 0.9 | 512 | 0.0572 | 0.8933 | 0.8462 | 0.8691 | 0.9832 | | 0.094 | 1.02 | 576 | 0.0728 | 0.8520 | 0.8681 | 0.8600 | 0.9795 | | 0.094 | 1.13 | 640 | 0.0784 | 0.8496 | 0.8717 | 0.8605 | 0.9800 | | 0.094 | 1.24 | 704 | 0.0721 | 0.8868 | 0.8527 | 0.8695 | 0.9814 | | 0.094 | 1.36 | 768 | 0.0700 | 0.8755 | 0.8362 | 0.8554 | 0.9808 | | 0.094 | 1.47 | 832 | 0.0590 | 0.8662 | 0.8610 | 0.8636 | 0.9822 | | 0.094 | 1.58 | 896 | 0.0615 | 0.8692 | 0.8764 | 0.8728 | 0.9821 | | 0.094 | 1.7 | 960 | 0.0670 | 0.8812 | 0.8557 | 0.8683 | 0.9826 | | 0.0413 | 1.81 | 1024 | 0.0623 | 0.9061 | 0.8557 | 0.8802 | 0.9843 | | 0.0413 | 1.92 | 1088 | 0.0570 | 0.8891 | 0.8770 | 0.8830 | 0.9833 | | 0.0413 | 2.04 | 1152 | 0.0643 | 0.8859 | 0.8859 | 0.8859 | 0.9831 | | 0.0413 | 2.15 | 1216 | 0.0705 | 0.8824 | 0.8740 | 0.8782 | 0.9830 | | 0.0413 | 2.26 | 1280 | 0.0698 | 0.8818 | 0.8557 | 0.8685 | 0.9824 | | 0.0413 | 2.37 | 1344 | 0.0826 | 0.8904 | 0.8693 | 0.8797 | 0.9832 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
connectivity/feather_berts_15
30574a68bff7ce81225235d8c766fb936547963d
2022-05-21T14:27:53.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/feather_berts_15
7
null
transformers
14,429
Entry not found
connectivity/feather_berts_44
6960cc352fa81ea9cf4b3fc5aa67e6c7b188c4f9
2022-05-21T14:28:54.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/feather_berts_44
7
null
transformers
14,430
Entry not found
connectivity/feather_berts_99
8df41b4f6982e33855cc2bf9547fe970195aa877
2022-05-21T14:31:06.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/feather_berts_99
7
null
transformers
14,431
Entry not found
connectivity/bert_ft_qqp-0
df16873724715a510537ba690937349ed5f78e0f
2022-05-21T16:30:57.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-0
7
null
transformers
14,432
Entry not found
connectivity/bert_ft_qqp-2
8db10ad4228b58e276599907be10e68af227bac3
2022-05-21T16:31:08.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-2
7
null
transformers
14,433
Entry not found
connectivity/bert_ft_qqp-3
518fdfe86d7486cfd4236507bc12280c8d6a4900
2022-05-21T16:31:11.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-3
7
null
transformers
14,434
Entry not found
connectivity/bert_ft_qqp-4
5b625109533c930996caa0123dc3b62810229e40
2022-05-21T16:31:15.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-4
7
null
transformers
14,435
Entry not found
connectivity/bert_ft_qqp-11
414286baa930fe6ddf6930c530824f4d07fbc956
2022-05-21T16:31:49.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-11
7
null
transformers
14,436
Entry not found
north/t5_large_NCC
e9ccebf514a58de7c7c32efc6d3d643965074c3d
2022-06-01T19:41:38.000Z
[ "pytorch", "tf", "jax", "tensorboard", "t5", "text2text-generation", "no", "nn", "sv", "dk", "is", "en", "dataset:nbailab/NCC", "dataset:mc4", "dataset:wikipedia", "arxiv:2104.09617", "arxiv:1910.10683", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
north
null
north/t5_large_NCC
7
null
transformers
14,437
--- language: - no - nn - sv - dk - is - en datasets: - nbailab/NCC - mc4 - wikipedia widget: - text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsråd på <extra_id_1>. Dette organet er øverste <extra_id_2> i Norge. For at møtet skal være <extra_id_3>, må over halvparten av regjeringens <extra_id_4> være til stede. - text: På <extra_id_0> kan man <extra_id_1> en bok, og man kan også <extra_id_2> seg ned og lese den. license: apache-2.0 --- -T5 The North-T5-models are a set of Norwegian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to translation. | |**Small** <br />_60M_|**Base** <br />_220M_|**Large** <br />_770M_|**XL** <br />_3B_|**XXL** <br />_11B_| |:-----------|:------------:|:------------:|:------------:|:------------:|:------------:| |North-T5&#8209;NCC|[🤗](https://huggingface.co/north/t5_small_NCC)|[🤗](https://huggingface.co/north/t5_base_NCC)|✔|[🤗](https://huggingface.co/north/t5_xl_NCC)|[🤗](https://huggingface.co/north/t5_xxl_NCC)|| |North-T5&#8209;NCC&#8209;lm|[🤗](https://huggingface.co/north/t5_small_NCC_lm)|[🤗](https://huggingface.co/north/t5_base_NCC_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)|[🤗](https://huggingface.co/north/t5_xxl_NCC_lm)|| ## T5X Checkpoint The original T5X checkpoint is also available for this model in the [Google Cloud Bucket](gs://north-t5x/pretrained_models/large/norwegian_NCC_plus_English_t5x_large/). ## Performance A thorough evaluation of the North-T5 models is planned, and I strongly recommend external researchers to make their own evaluation. The main advantage with the T5-models are their flexibility. Traditionally, encoder-only models (like BERT) excels in classification tasks, while seq-2-seq models are easier to train for tasks like translation and Q&A. Despite this, here are the results from using North-T5 on the political classification task explained [here](https://arxiv.org/abs/2104.09617). |**Model:** | **F1** | |:-----------|:------------| |mT5-base|73.2 | |mBERT-base|78.4 | |NorBERT-base|78.2 | |North-T5-small|80.5 | |nb-bert-base|81.8 | |North-T5-base|85.3 | |North-T5-large|86.7 | |North-T5-xl|88.7 | |North-T5-xxl|91.8| These are preliminary results. The [results](https://arxiv.org/abs/2104.09617) from the BERT-models are based on the test-results from the best model after 10 runs with early stopping and a decaying learning rate. The T5-results are the average of five runs on the evaluation set. The small-model was trained for 10.000 steps, while the rest for 5.000 steps. A fixed learning rate was used (no decay), and no early stopping. Neither was the recommended rank classification used. We use a max sequence length of 512. This method simplifies the test setup and gives results that are easy to interpret. However, the results from the T5 model might actually be a bit sub-optimal. ## Sub-versions of North-T5 The following sub-versions are available. More versions will be available shorter. |**Model** | **Description** | |:-----------|:-------| |**North&#8209;T5&#8209;NCC** |This is the main version. It is trained an additonal 500.000 steps on from the mT5 checkpoint. The training corpus is based on [the Norwegian Colossal Corpus (NCC)](https://huggingface.co/datasets/NbAiLab/NCC). In addition there are added data from MC4 and English Wikipedia.| |**North&#8209;T5&#8209;NCC&#8209;lm**|The model is pretrained for an addtional 100k steps on the LM objective discussed in the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf). In a way this turns a masked language model into an autoregressive model. It also prepares the model for some tasks. When for instance doing translation and NLI, it is well documented that there is a clear benefit to do a step of unsupervised LM-training before starting the finetuning.| ## Fine-tuned versions As explained below, the model really needs to be fine-tuned for specific tasks. This procedure is relatively simple, and the models are not very sensitive to the hyper-parameters used. Usually a decent result can be obtained by using a fixed learning rate of 1e-3. Smaller versions of the model typically needs to be trained for a longer time. It is easy to train the base-models in a Google Colab. Since some people really want to see what the models are capable of, without going through the training procedure, I provide a couple of test models. These models are by no means optimised, and are just for demonstrating how the North-T5 models can be used. * Nynorsk Translator. Translates any text from Norwegian Bokmål to Norwegian Nynorsk. Please test the [Streamlit-demo](https://huggingface.co/spaces/north/Nynorsk) and the [HuggingFace repo](https://huggingface.co/north/demo-nynorsk-base) * DeUnCaser. The model adds punctation, spaces and capitalisation back into the text. The input needs to be in Norwegian but does not have to be divided into sentences or have proper capitalisation of words. You can even remove the spaces from the text, and make the model reconstruct it. It can be tested with the [Streamlit-demo](https://huggingface.co/spaces/north/DeUnCaser) and directly on the [HuggingFace repo](https://huggingface.co/north/demo-deuncaser-base) ## Training details All models are built using the Flax-based T5X codebase, and all models are initiated with the mT5 pretrained weights. The models are trained using the T5.1.1 training regime, where they are only trained on an unsupervised masking-task. This also means that the models (contrary to the original T5) needs to be finetuned to solve specific tasks. This finetuning is however usually not very compute intensive, and in most cases it can be performed even with free online training resources. All the main model model versions are trained for 500.000 steps after the mT5 checkpoint (1.000.000 steps). They are trained mainly on a 75GB corpus, consisting of NCC, Common Crawl and some additional high quality English text (Wikipedia). The corpus is roughly 80% Norwegian text. Additional languages are added to retain some of the multilingual capabilities, making the model both more robust to new words/concepts and also more suited as a basis for translation tasks. While the huge models almost always will give the best results, they are also both more difficult and more expensive to finetune. I will strongly recommended to start with finetuning a base-models. The base-models can easily be finetuned on a standard graphic card or a free TPU through Google Colab. All models were trained on TPUs. The largest XXL model was trained on a TPU v4-64, the XL model on a TPU v4-32, the Large model on a TPU v4-16 and the rest on TPU v4-8. Since it is possible to reduce the batch size during fine-tuning, it is also possible to finetune on slightly smaller hardware. The rule of thumb is that you can go "one step down" when finetuning. The large models still rewuire access to significant hardware, even for finetuning. ## Formats All models are trained using the Flax-based T5X library. The original checkpoints are available in T5X format and can be used for both finetuning or interference. All models, except the XXL-model, are also converted to Transformers/HuggingFace. In this framework, the models can be loaded for finetuning or inference both in Flax, PyTorch and TensorFlow format. ## Future I will continue to train and release additional models to this set. What models that are added is dependent upon the feedbacki from the users ## Thanks This release would not have been possible without getting support and hardware from the [TPU Research Cloud](https://sites.research.google/trc/about/) at Google Research. Both the TPU Research Cloud Team and the T5X Team has provided extremely useful support for getting this running. Freddy Wetjen at the National Library of Norway has been of tremendous help in generating the original NCC corpus, and has also contributed to generate the collated coprus used for this training. In addition he has been a dicussion partner in the creation of these models. Also thanks to Stefan Schweter for writing the [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py) for converting these models from T5X to HuggingFace and to Javier de la Rosa for writing the dataloader for reading the HuggingFace Datasets in T5X. ## Warranty Use at your own risk. The models have not yet been thougroughly tested, and may contain both errors and biases. ## Contact/About These models were trained by Per E Kummervold. Please contact me on [email protected].
connectivity/cola_6ep_ft-39
5748587c89c41c0f46fb3d2fa7632bc8bd3a5ade
2022-05-21T16:43:55.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/cola_6ep_ft-39
7
null
transformers
14,438
Entry not found
connectivity/cola_6ep_ft-40
ef9f166f83ba7d1369aa076ed65ee6bc688d853b
2022-05-21T16:43:55.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/cola_6ep_ft-40
7
null
transformers
14,439
Entry not found
connectivity/cola_6ep_ft-43
04cc999c690c7f1d5328f55568c4211386beb441
2022-05-21T16:43:57.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/cola_6ep_ft-43
7
null
transformers
14,440
Entry not found
connectivity/cola_6ep_ft-45
e02d6c9d77124bb705ba3c88e03f5932f9ca00a8
2022-05-21T16:43:58.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/cola_6ep_ft-45
7
null
transformers
14,441
Entry not found
connectivity/cola_6ep_ft-47
da01cad61b621631691510605c197acb51b695a1
2022-05-21T16:43:59.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/cola_6ep_ft-47
7
null
transformers
14,442
Entry not found
connectivity/bert_ft_qqp-85
1a92bf2fbf6d06fafc49b92c6b9ae104255cb631
2022-05-21T16:37:34.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
connectivity
null
connectivity/bert_ft_qqp-85
7
null
transformers
14,443
Entry not found
globuslabs/ScholarBERT
88ed6b5d13eb476724adcc0ae59f039eef179fa7
2022-05-24T03:18:58.000Z
[ "pytorch", "bert", "fill-mask", "en", "arxiv:2205.11342", "transformers", "science", "multi-displinary", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
globuslabs
null
globuslabs/ScholarBERT
7
null
transformers
14,444
--- language: en tags: - science - multi-displinary license: apache-2.0 --- # ScholarBERT_100 Model This is the **ScholarBERT_100** variant of the ScholarBERT model family. The model is pretrained on a large collection of scientific research articles (**221B tokens**). This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default. The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters. # Model Architecture | Hyperparameter | Value | |-----------------|:-------:| | Layers | 24 | | Hidden Size | 1024 | | Attention Heads | 16 | | Total Parameters | 340M | # Training Dataset The vocab and the model are pertrained on **100% of the PRD** scientific literature dataset. The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”), a nonprofit organization based in California. This dataset was constructed from a corpus of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals. The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences, Social Sciences, and Technology. The distribution of articles is shown below. ![corpus pie chart](corpus_pie_chart.png) # BibTeX entry and citation info If using this model, please cite this paper: ``` @misc{hong2022scholarbert, doi = {10.48550/ARXIV.2205.11342}, url = {https://arxiv.org/abs/2205.11342}, author = {Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian}, title = {ScholarBERT: Bigger is Not Always Better}, publisher = {arXiv}, year = {2022} } ```
deutschmann/mdr-roberta-test
e8defb06f1270e3c8c7eefdb168f3287ed590cdb
2022-05-23T08:53:38.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
deutschmann
null
deutschmann/mdr-roberta-test
7
null
transformers
14,445
Entry not found
roschmid/distilbert-base-uncased-finetuned-TT2-exam
0118ca0b8118ec7e47841e8f79d37aca5116ddf0
2022-05-23T11:10:04.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
roschmid
null
roschmid/distilbert-base-uncased-finetuned-TT2-exam
7
null
transformers
14,446
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-TT2-exam results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9221537106364237 - name: Recall type: recall value: 0.9369056941492337 - name: F1 type: f1 value: 0.9294711725209478 - name: Accuracy type: accuracy value: 0.983509936931069 --- <!-- 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-TT2-exam This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Precision: 0.9222 - Recall: 0.9369 - F1: 0.9295 - Accuracy: 0.9835 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2509 | 1.0 | 879 | 0.0733 | 0.8855 | 0.9212 | 0.9030 | 0.9777 | | 0.0505 | 2.0 | 1758 | 0.0618 | 0.9221 | 0.9330 | 0.9275 | 0.9827 | | 0.0309 | 3.0 | 2637 | 0.0620 | 0.9222 | 0.9369 | 0.9295 | 0.9835 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
GioReg/dbmdzHateSpeech
19d28958cef24f7f3fd10ebda8727cc7e25c7a5e
2022-05-23T17:02:37.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
GioReg
null
GioReg/dbmdzHateSpeech
7
null
transformers
14,447
--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: dbmdzHateSpeech 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. --> # dbmdzHateSpeech This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7919 - Accuracy: 0.706 - F1: 0.3524 ## 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: 2 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
GioReg/mBERTnews
d32a233dffbbe43aedddfba7c451af9271b0e404
2022-05-23T18:02:16.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
GioReg
null
GioReg/mBERTnews
7
null
transformers
14,448
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: mBERTnews 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. --> # mBERTnews This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1136 - Accuracy: 0.9739 - F1: 0.9732 ## 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: 2 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
AfnanAl/mT5small-ArabicSummary
e83cc6c570051e0ed490fae096195be9a72d23aa
2022-05-25T05:00:01.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
AfnanAl
null
AfnanAl/mT5small-ArabicSummary
7
null
transformers
14,449
Entry not found
nam7197/vi-nli-xml-roberta-base
2189f9a6a08ffb3090201ba6f51f39922b291b18
2022-07-16T01:43:34.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
nam7197
null
nam7197/vi-nli-xml-roberta-base
7
null
transformers
14,450
# Dataset: https://huggingface.co/datasets/xnli/viewer/vi/train # Github: https://github.com/namlv97/vi-nli-xlm-roberta-base ```python >>> import torch >>> from transformers import AutoTokenizer,AutoModelForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base') >>> model=AutoModelForSequenceClassification.from_pretrained('nam7197/vi-nli-xml-roberta-base') >>> premise="Vâng, tôi thậm chí không nghĩ về điều đó, nhưng tôi đã rất thất vọng, và, tôi lại nói chuyện với anh ta lần nữa." >>> hypothesis="Tôi đã không nói chuyện với anh ta nữa." >>> label=2 #contradiction >>> inputs=tokenizer(premise,hypothesis,return_tensors='pt') >>> model.eval() >>> with torch.no_grad(): >>> outputs=model(**inputs) >>> probs= torch.nn.functional.softmax(outputs.logits,dim=-1) >>> pred_label=torch.argmax(probs,dim=-1) ``` # Performance | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | entailment | 0.79256 | 0.77784 | 0.78513 | 1670 | | neutral | 0.77192 | 0.70120 | 0.73486 | 1670 | | contradiction| 0.76429 | 0.84850 | 0.80420 | 1670 | | accuracy | | | 0.77585 | 5010 | | macro avg | 0.77626 | 0.77585 | 0.77473 | 5010 | | weighted avg | 0.77626 | 0.77585 | 0.77473 | 5010 |
jrmax/bart-base-r3d3
8cc752f8679d679816d62f930b7c0e23bc9dd9e3
2022-05-25T14:02:52.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
jrmax
null
jrmax/bart-base-r3d3
7
null
transformers
14,451
Entry not found
MadFace/t5-arxiv
aad48f3089ce650cd9d2463f42c206230fec877b
2022-05-26T08:00:38.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
MadFace
null
MadFace/t5-arxiv
7
null
transformers
14,452
--- tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-arxiv 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. --> # t5-arxiv This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3852 - Rouge1: 18.0722 - Rouge2: 6.8453 - Rougel: 14.3659 - Rougelsum: 16.4137 - Gen Len: 19.0 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.5169 | 1.0 | 12500 | 2.3852 | 18.0722 | 6.8453 | 14.3659 | 16.4137 | 19.0 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
alice-hml/mBART_french_correction
c7c580f39e2c0cf866d1b96171bcb6dc4a63a0de
2022-05-26T13:15:38.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "license:other", "autotrain_compatible" ]
text2text-generation
false
alice-hml
null
alice-hml/mBART_french_correction
7
null
transformers
14,453
--- license: other ---
aioxlabs/dvoice-amharic
cd233859f9c53f043d6d36b4d2eb7fad13545a45
2022-05-28T08:22:00.000Z
[ "wav2vec2", "feature-extraction", "dar", "dataset:commonvoice", "speechbrain", "CTC", "pytorch", "Transformer", "license:apache-2.0", "automatic-speech-recognition" ]
automatic-speech-recognition
false
aioxlabs
null
aioxlabs/dvoice-amharic
7
null
speechbrain
14,454
--- language: "dar" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - commonvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Amharic dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). | DVoice Release | Val. CER | Val. WER | Test CER | Test WER | |:-------------:|:---------------------------:| -----:| -----:| -----:| | v2.0 | 6.71 | 25.50 | 6.57 | 24.92 | # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. # Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Amharic) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-amharic", savedir="pretrained_models/asr-wav2vec2-dvoice-amh") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice). # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About DVoice DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social medias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke. For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
leonweber/foo
e986d212914fa6db9944d8affb699e6dbf59e6b8
2022-05-29T09:29:28.000Z
[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
leonweber
null
leonweber/foo
7
null
transformers
14,455
Entry not found
huggingtweets/algodtrading
8ab2d6821cd3189147da465a935b0041fdc552b3
2022-05-27T22:21:11.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/algodtrading
7
null
transformers
14,456
--- language: en thumbnail: http://www.huggingtweets.com/algodtrading/1653690066290/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1509493999987474434/nB7rOJnT_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Algod🫐</div> <div style="text-align: center; font-size: 14px;">@algodtrading</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Algod🫐. | Data | Algod🫐 | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 56 | | Short tweets | 391 | | Tweets kept | 2802 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mz6oljo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @algodtrading's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1oouvcmj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1oouvcmj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/algodtrading') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
hoanhtu/vi-large
54bb9f78672c171e8029c0adab5e37c7a1df9e5c
2022-06-05T08:36:06.000Z
[ "pytorch", "tf", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
hoanhtu
null
hoanhtu/vi-large
7
null
transformers
14,457
Entry not found
Anjoe/german-poetry-distilbert
f41de1f960e115b238081f713847376856141dfb
2022-07-21T14:27:30.000Z
[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Anjoe
null
Anjoe/german-poetry-distilbert
7
null
transformers
14,458
Entry not found
KoichiYasuoka/deberta-base-thai
a9b10052949afdfdf3700ba25d58d3ee636ab199
2022-07-16T10:52:25.000Z
[ "pytorch", "deberta-v2", "fill-mask", "th", "transformers", "thai", "masked-lm", "wikipedia", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
KoichiYasuoka
null
KoichiYasuoka/deberta-base-thai
7
null
transformers
14,459
--- language: - "th" tags: - "thai" - "masked-lm" - "wikipedia" license: "apache-2.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" --- # deberta-base-thai ## Model Description This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. You can fine-tune `deberta-base-thai` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-thai-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-thai-ud-head), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-thai") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-thai") ```
ceggian/bart_post_trained_reddit_batch64
b47ad5c15f68e74aa029261fbb48ed82a9935f8d
2022-05-30T18:05:26.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ceggian
null
ceggian/bart_post_trained_reddit_batch64
7
null
transformers
14,460
Entry not found
BigSalmon/InformalToFormalLincoln49
87440f099309bdc0cd2a14b9b772ffe6e971d284
2022-06-07T01:12:31.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/InformalToFormalLincoln49
7
null
transformers
14,461
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln49") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49") ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence. ``` ngos are characterized by: □ voluntary citizens' group that is organized on a local, national or international level □ encourage political participation □ often serve humanitarian functions □ work for social, economic, or environmental change *** what are the drawbacks of living near an airbnb? □ noise □ parking □ traffic □ security □ strangers *** ``` ``` original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung. adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung. *** original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark. adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark. *** original: ``` ``` original: had trouble deciding. translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation. *** original: ``` ``` input: not loyal 1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ). *** input: ```
malra/segformer-b0-finetuned-segments-sidewalk-4
4d91df6747492b36e06ad6c213271529a708c732
2022-05-31T15:42:53.000Z
[ "pytorch", "segformer", "transformers", "vision", "image-segmentation", "generated_from_trainer", "license:apache-2.0", "model-index" ]
image-segmentation
false
malra
null
malra/segformer-b0-finetuned-segments-sidewalk-4
7
null
transformers
14,462
--- license: apache-2.0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-4 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. --> # segformer-b0-finetuned-segments-sidewalk-4 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.5207 - Mean Iou: 0.1023 - Mean Accuracy: 0.1567 - Overall Accuracy: 0.6612 - Per Category Iou: [0.0, 0.37997208823402434, 0.7030895600821837, 0.0, 0.0020740824048893942, 0.0006611109803275343, 0.0, 0.0009644717061794479, 0.0, 0.0, 0.44780560238339745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4962679673706645, 0.0, 0.008267299447856608, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6719286019431624, 0.1932540547332544, 0.6762198255750292, 0.0, 0.0, 0.0003312368464636427, 0.0] - Per Category Accuracy: [nan, 0.7085417733756095, 0.8643251797889624, 0.0, 0.0020922282164545967, 0.0006691672739475508, nan, 0.0009725011389865425, 0.0, 0.0, 0.9224475476880146, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7984415122785299, 0.0, 0.008394275137866055, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9294223049507054, 0.2306496542338313, 0.7045666997791757, 0.0, 0.0, 0.0003315891206418271, 0.0] ## 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: 6e-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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 2.8255 | 1.0 | 25 | 3.0220 | 0.0892 | 0.1429 | 0.6352 | [0.0, 0.3631053229188519, 0.6874502125236047, 0.0, 0.012635239862746197, 0.001133215250040838, 0.0, 0.00463024415429387, 2.6557099661207286e-05, 0.0, 0.3968535016422742, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4820466790242289, 0.0, 0.00693999220077067, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6134928158666486, 0.05160593984758798, 0.5016270369795023, 0.0, 0.0, 0.00023524914354608678, 0.0] | [nan, 0.6625398055826, 0.851744092156527, 0.0, 0.01307675614921835, 0.001170877257777663, nan, 0.004771009467501389, 2.6941417811356193e-05, 0.0, 0.9316713675735513, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7310221003907382, 0.0, 0.0070371168820434, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.948375993368795, 0.056265031783493576, 0.5061367774453964, 0.0, 0.0, 0.00023723449281691698, 0.0] | | 2.5443 | 2.0 | 50 | 2.5207 | 0.1023 | 0.1567 | 0.6612 | [0.0, 0.37997208823402434, 0.7030895600821837, 0.0, 0.0020740824048893942, 0.0006611109803275343, 0.0, 0.0009644717061794479, 0.0, 0.0, 0.44780560238339745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4962679673706645, 0.0, 0.008267299447856608, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6719286019431624, 0.1932540547332544, 0.6762198255750292, 0.0, 0.0, 0.0003312368464636427, 0.0] | [nan, 0.7085417733756095, 0.8643251797889624, 0.0, 0.0020922282164545967, 0.0006691672739475508, nan, 0.0009725011389865425, 0.0, 0.0, 0.9224475476880146, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7984415122785299, 0.0, 0.008394275137866055, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9294223049507054, 0.2306496542338313, 0.7045666997791757, 0.0, 0.0, 0.0003315891206418271, 0.0] | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
yukta10/finetuning-sentiment-model-3000-samples
32985a807eb6110f939af0c0821ef62dda76ed81
2022-05-31T18:29:16.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
yukta10
null
yukta10/finetuning-sentiment-model-3000-samples
7
null
transformers
14,463
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [federicopascual/finetuning-sentiment-model-3000-samples](https://huggingface.co/federicopascual/finetuning-sentiment-model-3000-samples) on the imdb 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: 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: 2 ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
joaogante/test_text
e7e523d376bfb794ce53256716572d91e87bf46d
2022-06-15T16:53:59.000Z
[ "pytorch", "tf", "jax", "rust", "distilbert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1910.01108", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
joaogante
null
joaogante/test_text
7
null
transformers
14,464
--- language: en tags: - exbert license: apache-2.0 datasets: - bookcorpus - wikipedia --- # DistilBERT base model (uncased) This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is uncased: it does not make a difference between english and English. ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: - Distillation loss: the model was trained to return the same probabilities as the BERT base model. - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model. This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.05292855575680733, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.03968575969338417, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a business model. [SEP]", 'score': 0.034743521362543106, 'token': 2449, 'token_str': 'business'}, {'sequence': "[CLS] hello i'm a model model. [SEP]", 'score': 0.03462274372577667, 'token': 2944, 'token_str': 'model'}, {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 'score': 0.018145186826586723, 'token': 11643, 'token_str': 'modeling'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("The White man worked as a [MASK].") [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', 'score': 0.1235365942120552, 'token': 20987, 'token_str': 'blacksmith'}, {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', 'score': 0.10142576694488525, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the white man worked as a farmer. [SEP]', 'score': 0.04985016956925392, 'token': 7500, 'token_str': 'farmer'}, {'sequence': '[CLS] the white man worked as a miner. [SEP]', 'score': 0.03932540491223335, 'token': 18594, 'token_str': 'miner'}, {'sequence': '[CLS] the white man worked as a butcher. [SEP]', 'score': 0.03351764753460884, 'token': 14998, 'token_str': 'butcher'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', 'score': 0.13283951580524445, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.12586183845996857, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a maid. [SEP]', 'score': 0.11708822101354599, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', 'score': 0.11499975621700287, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', 'score': 0.04722772538661957, 'token': 22583, 'token_str': 'housekeeper'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 16 GB V100 for 90 hours. See the [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters details. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | ### BibTeX entry and citation info ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
malra/segformer-b5-segments-warehouse1
9a8a20c7811870c7f3e9db71acc2bc80b882d562
2022-05-31T20:54:00.000Z
[ "pytorch", "segformer", "transformers", "vision", "image-segmentation", "generated_from_trainer", "license:apache-2.0", "model-index" ]
image-segmentation
false
malra
null
malra/segformer-b5-segments-warehouse1
7
null
transformers
14,465
--- license: apache-2.0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b5-segments-warehouse1 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. --> # segformer-b5-segments-warehouse1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the jakka/warehouse_part1 dataset. It achieves the following results on the evaluation set: - Loss: 0.1610 - Mean Iou: 0.6952 - Mean Accuracy: 0.8014 - Overall Accuracy: 0.9648 - Per Category Iou: [0.0, 0.47153295365063086, 0.9293854681828234, 0.9766069961659746, 0.927007550222462, 0.9649404794739765, 0.9824606440795911, 0.8340592613982738, 0.9706739467997174, 0.653761891900003, 0.0, 0.8080046149867717, 0.75033588410538, 0.6921465280057791, 0.7522124809345331, 0.7548461579766955, 0.3057219434101416, 0.5087799410519325, 0.84829211455404, 0.7730356409704979] - Per Category Accuracy: [nan, 0.9722884260421271, 0.9720560851996344, 0.9881427437833682, 0.9650114633107388, 0.9828538231066912, 0.9897027752946145, 0.9071521422402136, 0.9848998109819413, 0.6895634832705517, 0.0, 0.8704126720181029, 0.8207667731629393, 0.7189631369929214, 0.8238982104266324, 0.8620090549531412, 0.3522998155172771, 0.5387075151368637, 0.9081104400345125, 0.8794092789466661] ## 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: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.1656 | 1.0 | 787 | 0.1917 | 0.5943 | 0.6937 | 0.9348 | [0.0, 0.8760430595457738, 0.8113714411434076, 0.9533787339343942, 0.8499988352439646, 0.9330256290984922, 0.964368918196211, 0.6984009498117659, 0.9341093239597545, 0.288411561596369, 0.0, 0.6496866199024376, 0.4510074387900882, 0.5206343319728309, 0.6377305875444397, 0.5391733301507737, 0.1395685713288422, 0.390702947845805, 0.6999919374344916, 0.548023343373494] | [nan, 0.9502542152644661, 0.9516900451328754, 0.9788975544390225, 0.921821413759201, 0.9534230318615367, 0.9778020069070933, 0.8108538425970355, 0.970571911491369, 0.2993067645848501, 0.0, 0.7454496363566233, 0.5849840255591054, 0.5858306866277158, 0.7137540570947559, 0.6925710548100606, 0.16576498144808574, 0.4165357186026834, 0.8142326593390103, 0.6474578532983408] | | 0.0948 | 2.0 | 1574 | 0.2058 | 0.6310 | 0.7305 | 0.9442 | [0.0, 0.904077233776714, 0.8616556242304713, 0.9604692135700761, 0.8306854004041632, 0.9459690932012119, 0.9714777936344227, 0.7463801249809481, 0.9197830038961162, 0.4759644364074744, 0.0, 0.7133768631713745, 0.4878118726699168, 0.5403469048526253, 0.6267211124010835, 0.6280780328151242, 0.11116434156063161, 0.4757211293446132, 0.7386220435315599, 0.6814722192019137] | [nan, 0.9530795697109564, 0.9481439135801821, 0.9753750826203033, 0.9328161802391284, 0.9783733696392768, 0.9831560736299451, 0.8544532947139754, 0.9700176894451403, 0.5598936405938401, 0.0, 0.8212854589792271, 0.5434504792332269, 0.5765256977221256, 0.7602586827898242, 0.745275787709383, 0.12024542420662065, 0.5128732019823522, 0.8080522939565592, 0.8363729371469241] | | 0.0595 | 3.0 | 2361 | 0.1363 | 0.6578 | 0.7540 | 0.9494 | [0.0, 0.9109388123768081, 0.8466263269727539, 0.965583073696094, 0.8848508600101197, 0.9507919193853351, 0.9742807972055659, 0.7672266040033193, 0.9571650494933543, 0.5580972230045627, 0.0, 0.7572676505482382, 0.5338298840118263, 0.5743160573368553, 0.6964399439112182, 0.6369583059750492, 0.19255896751223853, 0.49017131449756574, 0.7563405327946686, 0.7018448645266491] | [nan, 0.9587813659877967, 0.9568298005631468, 0.9842947615263231, 0.9380059570384915, 0.9734457175747111, 0.9839202800499454, 0.863077218359317, 0.9757816512090675, 0.6272609287455287, 0.0, 0.8589569413670591, 0.5999361022364217, 0.6161844118746441, 0.7983763527021668, 0.793146442915981, 0.2242190576871256, 0.5288397085810358, 0.8216978654762351, 0.8232729860771318] | | 0.0863 | 4.0 | 3148 | 0.1706 | 0.6597 | 0.7678 | 0.9537 | [0.0, 0.5911845175607978, 0.8922572171811833, 0.9657396689703207, 0.8726664918778465, 0.948172990516989, 0.9741643734457509, 0.7832072821045744, 0.9578631876788363, 0.5869565217391305, 0.0, 0.7602876424039574, 0.5747447162194254, 0.6642950791717092, 0.6978602093118107, 0.7122118073263809, 0.21745086578505152, 0.5091171801864137, 0.763416879968237, 0.7220314268720861] | [nan, 0.9656626144746107, 0.9588916966191391, 0.9766109980050623, 0.9234167566678667, 0.9783156758536367, 0.9891284919047324, 0.8876447135391675, 0.9773653302095363, 0.6623721946123896, 0.0, 0.8391697702425289, 0.6185942492012779, 0.6961703584876796, 0.8060121894956657, 0.8277923697200732, 0.24677155234956366, 0.5498060503499884, 0.8475353565667555, 0.8369956852453183] | | 0.0849 | 5.0 | 3935 | 0.1529 | 0.6489 | 0.7616 | 0.9535 | [0.0, 0.34717493700692625, 0.9200786785121082, 0.9707860061715432, 0.9064316496153364, 0.9571373496125165, 0.9765647396031262, 0.7914886053951578, 0.9636858999629485, 0.5253852888123762, 0.0, 0.7668434757450091, 0.6228696113699357, 0.5646135260344276, 0.7194371537530142, 0.7276571750775304, 0.13134474327628362, 0.5398065590178835, 0.8087983436006237, 0.7371620697069805] | [nan, 0.9673995855258336, 0.9622823082917784, 0.9832096263122092, 0.9590923200613435, 0.9794833291868915, 0.9849481430590119, 0.8741570190973889, 0.9814726613968338, 0.5661042702035389, 0.0, 0.8519369313384734, 0.674888178913738, 0.5955861885708164, 0.7973710835377057, 0.8440933293815855, 0.139191177994735, 0.5807830511082053, 0.8902258318640507, 0.8387304835194164] | | 0.0652 | 6.0 | 4722 | 0.1776 | 0.6701 | 0.7802 | 0.9598 | [0.0, 0.442020662403383, 0.9221209597093164, 0.9723970198449976, 0.9094898951877407, 0.958969887541612, 0.9774286126326331, 0.8043337900190548, 0.9641322534475246, 0.524194500874002, 0.0, 0.7732021981650511, 0.6714277552419585, 0.6791383524722951, 0.7265590222386986, 0.7252668038047013, 0.25612624095650144, 0.512317443386938, 0.8223912256195354, 0.7602526763224181] | [nan, 0.9667776521571092, 0.968306375662177, 0.9871287057126554, 0.9515142073239339, 0.9800501491032743, 0.9870913605013194, 0.8911998464531551, 0.9789458602211063, 0.5619638504637396, 0.0, 0.8429926328466184, 0.750926517571885, 0.7091730161871252, 0.8058454540303847, 0.8431735260151052, 0.2957320232987169, 0.5489159698031933, 0.8944742469145065, 0.8592366887593968] | | 0.0516 | 7.0 | 5509 | 0.2204 | 0.6782 | 0.7854 | 0.9562 | [0.0, 0.5972965874238374, 0.9024890361234837, 0.9727685140940331, 0.915582953759141, 0.9598962357171329, 0.9798718588278901, 0.8112726586102719, 0.9047252363294271, 0.6408527982442389, 0.0, 0.7886848740988032, 0.676712646342877, 0.5672950158399087, 0.7336613818739761, 0.7298649456617311, 0.3028603088856569, 0.5060868673401364, 0.8269845785168136, 0.7471687598272396] | [nan, 0.9698273468544609, 0.9632905651879291, 0.9861640741314249, 0.9551792854314081, 0.9817079843391511, 0.9899518141518776, 0.8996100259110301, 0.9832172012468946, 0.6987812984710835, 0.0, 0.8565569379384828, 0.7460702875399361, 0.593452450290354, 0.8111955580377016, 0.848355084979611, 0.3625810998486827, 0.5422458600265925, 0.8997261507296395, 0.834927271918509] | | 0.1051 | 8.0 | 6296 | 0.1860 | 0.6731 | 0.7789 | 0.9575 | [0.0, 0.44805540920356957, 0.9045125103512419, 0.9742941726927242, 0.9171717803896707, 0.9608739687771942, 0.9806696534895757, 0.8165927346840907, 0.9677688538979997, 0.6195552331193943, 0.0, 0.795984684169727, 0.6862710467443778, 0.573071397774824, 0.7390593444665892, 0.746059006435751, 0.2037963564144674, 0.5303406505500898, 0.8387988518436741, 0.7590468131997875] | [nan, 0.9709112878685233, 0.966379770128131, 0.9872427322752713, 0.9529925896087971, 0.9834568092767589, 0.9900317817435064, 0.8913394344939497, 0.9851288999243455, 0.6704124592447216, 0.0, 0.871338387626268, 0.7448562300319489, 0.5994265432176736, 0.8121846392929121, 0.8435414473616973, 0.2212134402918558, 0.5609595288067426, 0.8906947518475448, 0.8579244695520661] | | 0.0619 | 9.0 | 7083 | 0.2919 | 0.6996 | 0.7903 | 0.9579 | [0.0, 0.934913158921961, 0.9053172937262943, 0.9749731654503406, 0.8705131863049136, 0.9625421596476281, 0.9801264786114002, 0.8223383305806123, 0.9066864104553713, 0.6468175775129386, 0.0, 0.7950479182280621, 0.7176821075997429, 0.5689160215594734, 0.7424713897302829, 0.7480081111150989, 0.3071719253739231, 0.5035704204000125, 0.8359422295252097, 0.7696666024282135] | [nan, 0.9682325320018036, 0.9702179964865137, 0.9871538608460199, 0.9606411126417358, 0.9816951395784177, 0.9890656141613147, 0.9035010425481796, 0.9836680314909386, 0.689949669209585, 0.0, 0.8547140781629688, 0.7850479233226837, 0.5903872774743949, 0.8138309496636962, 0.8520138583707216, 0.3614203096822337, 0.5292682658813446, 0.9065161120906329, 0.8882611983452693] | | 0.081 | 10.0 | 7870 | 0.2470 | 0.6804 | 0.7921 | 0.9583 | [0.0, 0.4404433924045006, 0.9318621565838054, 0.9751204660574527, 0.8701648407446415, 0.9625333515302946, 0.9811772580795882, 0.8257730976318673, 0.9694596723226286, 0.6262599628453287, 0.0, 0.8035308913444122, 0.7247258740455824, 0.5731919576321138, 0.7446832704519876, 0.7540709586972932, 0.2964031339031339, 0.5176075672651548, 0.8402309249924604, 0.7699341552529259] | [nan, 0.9683524762943433, 0.9703483634609842, 0.9874040565137937, 0.9560906426120769, 0.9828287794111833, 0.9897414692905638, 0.9071739528715878, 0.9809845681174846, 0.6616061536513564, 0.0, 0.8707555296507566, 0.8066453674121405, 0.5982298533423343, 0.8269010675926151, 0.8575633386818196, 0.3450448769769707, 0.5489928903442743, 0.9145158870090407, 0.8764289844757795] | | 0.0595 | 11.0 | 8657 | 0.1520 | 0.6754 | 0.7803 | 0.9583 | [0.0, 0.43998949915443775, 0.9316636729918347, 0.974311900634481, 0.90408659589869, 0.9621039259469353, 0.9814528086580536, 0.8173484866921386, 0.9299168519752622, 0.5981595278841879, 0.0, 0.79896542666047, 0.7130791649318979, 0.5767892232828117, 0.7434904893608313, 0.7476740572849074, 0.2818679619421856, 0.5013427236914975, 0.8417679322268942, 0.7636900967723242] | [nan, 0.9604694708457627, 0.9682111157218825, 0.9850226034689381, 0.9629913194164226, 0.9838887233262218, 0.9906282066977372, 0.8790295141463755, 0.9828138682520776, 0.6217973473457631, 0.0, 0.8472869246956067, 0.7660702875399361, 0.601589754313674, 0.8233235396482367, 0.8360910400932068, 0.3211657649814481, 0.5272243772183335, 0.8880687999399782, 0.8793425559361239] | | 0.0607 | 12.0 | 9444 | 0.1907 | 0.6792 | 0.7814 | 0.9611 | [0.0, 0.4394265102382861, 0.9325678358934418, 0.9751503005414947, 0.9213536629526586, 0.9630218995457999, 0.9808145244188059, 0.8160516650442948, 0.9402095421968347, 0.5678403556289702, 0.0, 0.7897903639847522, 0.717973174366617, 0.6351749265433101, 0.7451406149738536, 0.7539060338307724, 0.2810049109433409, 0.5169863186167534, 0.8447414560224139, 0.7628612943763745] | [nan, 0.964392093449931, 0.9699039597844642, 0.9860071181495944, 0.9689476561441872, 0.9817555601847723, 0.9915172012546744, 0.8703445207331861, 0.9829836512368835, 0.5919660662847014, 0.0, 0.8320126171608817, 0.7695846645367412, 0.6606869598697208, 0.8177192854656857, 0.8353858575122385, 0.31786995004456603, 0.541465665967056, 0.8991915819484563, 0.8640852275254659] | | 0.054 | 13.0 | 10231 | 0.1756 | 0.6845 | 0.7854 | 0.9633 | [0.0, 0.44063089620853896, 0.9319015227980866, 0.9747420439658205, 0.9230841377589553, 0.9626774348954341, 0.9806204202647846, 0.824089995398513, 0.9682449901582629, 0.6269069221957562, 0.0, 0.7878031759942226, 0.7230044147476434, 0.6870255399578931, 0.7273836360818303, 0.7465091396254238, 0.25750268946841265, 0.5202245077135331, 0.8455619310735664, 0.7623883906475817] | [nan, 0.9684613146338701, 0.9659761462687484, 0.985573907589379, 0.969242630837417, 0.9846717514218756, 0.9904148523034052, 0.8905935109009535, 0.9873657317056209, 0.6548320724256909, 0.0, 0.8321711888159841, 0.7743769968051119, 0.7167465941354711, 0.7672955669410517, 0.8485288256155018, 0.28777231930020936, 0.5469380130325374, 0.8955527628765427, 0.8564788043236511] | | 0.0908 | 14.0 | 11018 | 0.1677 | 0.6922 | 0.7956 | 0.9641 | [0.0, 0.4710389646938612, 0.9277225664822271, 0.9753445134184554, 0.9250469473155007, 0.9640090632546157, 0.9817333061419466, 0.8297056239192101, 0.970059681920668, 0.647379308685926, 0.0, 0.79693329490141, 0.7458423929012165, 0.6895638439061885, 0.7486849253355593, 0.7520096317485606, 0.30687537928818764, 0.49287677819238446, 0.848826224760963, 0.7700556938025832] | [nan, 0.9666066204807101, 0.9697912533607226, 0.9863864033340946, 0.9658514745108883, 0.9826761492096202, 0.9913739259863396, 0.9020659030037601, 0.9838249561044068, 0.6815485423063531, 0.0, 0.8412997732853904, 0.8109904153354632, 0.7185046709734403, 0.8232134618653327, 0.8490091673735526, 0.35638330949567815, 0.5181697306682197, 0.9016768578609746, 0.8671989680174369] | | 0.0584 | 15.0 | 11805 | 0.1610 | 0.6952 | 0.8014 | 0.9648 | [0.0, 0.47153295365063086, 0.9293854681828234, 0.9766069961659746, 0.927007550222462, 0.9649404794739765, 0.9824606440795911, 0.8340592613982738, 0.9706739467997174, 0.653761891900003, 0.0, 0.8080046149867717, 0.75033588410538, 0.6921465280057791, 0.7522124809345331, 0.7548461579766955, 0.3057219434101416, 0.5087799410519325, 0.84829211455404, 0.7730356409704979] | [nan, 0.9722884260421271, 0.9720560851996344, 0.9881427437833682, 0.9650114633107388, 0.9828538231066912, 0.9897027752946145, 0.9071521422402136, 0.9848998109819413, 0.6895634832705517, 0.0, 0.8704126720181029, 0.8207667731629393, 0.7189631369929214, 0.8238982104266324, 0.8620090549531412, 0.3522998155172771, 0.5387075151368637, 0.9081104400345125, 0.8794092789466661] | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
neal49/roberta-yelp
0e3c4c84ab49ed8c77781637579d31875c0bb9b0
2022-06-01T05:39:18.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
neal49
null
neal49/roberta-yelp
7
null
transformers
14,466
Entry not found
Paoloant/distilbert-base-uncased-finetuned-emotion
d28fd928b4ee2abe6da5a40858aedbe8fce09625
2022-06-01T19:02:01.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
Paoloant
null
Paoloant/distilbert-base-uncased-finetuned-emotion
7
null
transformers
14,467
Entry not found
ederkamphorst/bert-base-portuguese-cased-finetuned-acordao_v2
c22fe00178bd9882da169fe9a2e732e0a330ae1f
2022-06-02T03:37:32.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
fill-mask
false
ederkamphorst
null
ederkamphorst/bert-base-portuguese-cased-finetuned-acordao_v2
7
null
transformers
14,468
--- license: mit tags: - generated_from_trainer model-index: - name: bert-base-portuguese-cased-finetuned-acordao_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-portuguese-cased-finetuned-acordao_v2 This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9156 ## 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9871 | 1.0 | 313 | 0.9519 | | 0.965 | 2.0 | 626 | 0.9325 | | 0.9501 | 3.0 | 939 | 0.9257 | | 0.929 | 4.0 | 1252 | 0.9098 | | 0.9276 | 5.0 | 1565 | 0.9018 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
leonweber/biomuppet
9d3b2af26fa416c59c6fdad63d0e6baf10afde9f
2022-06-03T09:58:25.000Z
[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
leonweber
null
leonweber/biomuppet
7
null
transformers
14,469
Entry not found
Classroom-workshop/assignment1-jane
9d87d97585346301ee7677baaecf74ec93ebbf3e
2022-06-02T15:21:22.000Z
[ "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2010.05171", "arxiv:1904.08779", "transformers", "speech", "audio", "hf-asr-leaderboard", "license:mit", "model-index" ]
automatic-speech-recognition
false
Classroom-workshop
null
Classroom-workshop/assignment1-jane
7
null
transformers
14,470
--- language: en datasets: - librispeech_asr tags: - speech - audio - automatic-speech-recognition - hf-asr-leaderboard license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: s2t-small-librispeech-asr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 4.3 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 9.0 --- # S2T-SMALL-LIBRISPEECH-ASR `s2t-small-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard autoregressive cross-entropy loss and generates the transcripts autoregressively. ## Intended uses & limitations This model can be used for end-to-end speech recognition (ASR). See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* *Note: The feature extractor depends on [torchaudio](https://github.com/pytorch/audio) and the tokenizer depends on [sentencepiece](https://github.com/google/sentencepiece) so be sure to install those packages before running the examples.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) input_features = processor( ds[0]["audio"]["array"], sampling_rate=16_000, return_tensors="pt" ).input_features # Batch size 1 generated_ids = model.generate(input_ids=input_features) transcription = processor.batch_decode(generated_ids) ``` #### Evaluation on LibriSpeech Test The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset. ```python from datasets import load_dataset, load_metric from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset wer = load_metric("wer") model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to("cuda") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr", do_upper_case=True) librispeech_eval = librispeech_eval.map(map_to_array) def map_to_pred(batch): features = processor(batch["audio"]["array"], sampling_rate=16000, padding=True, return_tensors="pt") input_features = features.input_features.to("cuda") attention_mask = features.attention_mask.to("cuda") gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask) batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True) return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"]) print("WER:", wer(predictions=result["transcription"], references=result["text"])) ``` *Result (WER)*: | "clean" | "other" | |:-------:|:-------:| | 4.3 | 9.0 | ## Training data The S2T-SMALL-LIBRISPEECH-ASR is trained on [LibriSpeech ASR Corpus](https://www.openslr.org/12), a dataset consisting of approximately 1000 hours of 16kHz read English speech. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 10,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
gciaffoni/wav2vec2-large-xls-r-300m-it-colab
bf62ab8ae04133f2ac6f6949e53ae3bb6881b48b
2022-06-02T22:10:02.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
gciaffoni
null
gciaffoni/wav2vec2-large-xls-r-300m-it-colab
7
null
transformers
14,471
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-it-colab 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. --> # wav2vec2-large-xls-r-300m-it-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1660 - Wer: 0.1648 ## 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: 0.0003 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.5632 | 3.19 | 1000 | 0.2289 | 0.2470 | | 0.1489 | 6.39 | 2000 | 0.1799 | 0.1877 | | 0.0803 | 9.58 | 3000 | 0.1660 | 0.1648 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
kevincstowe/concept2seq
b90f1d48f02cb08bc4fac86df4bb565250be01d1
2022-06-07T17:42:01.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
kevincstowe
null
kevincstowe/concept2seq
7
null
transformers
14,472
Entry not found
Jeevesh8/lecun_feather_berts-7
e7fb1f8baeb9d5ac92371c0ac20dd54b7b5bd24c
2022-06-04T06:52:18.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/lecun_feather_berts-7
7
null
transformers
14,473
Entry not found
mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling
bb430adbfd6619fdd09b45856d7b7886738ddd96
2022-06-05T18:12:43.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
mmillet
null
mmillet/rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling
7
null
transformers
14,474
--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling 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. --> # rubert-tiny2_finetuned_emotion_experiment_modified_CE_LOSS_resampling This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4520 - Accuracy: 0.8621 - F1: 0.8616 ## 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: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1134 | 1.0 | 53 | 0.9494 | 0.7716 | 0.7555 | | 0.8421 | 2.0 | 106 | 0.7092 | 0.8204 | 0.8172 | | 0.6488 | 3.0 | 159 | 0.6000 | 0.8319 | 0.8313 | | 0.5392 | 4.0 | 212 | 0.5368 | 0.8376 | 0.8392 | | 0.4616 | 5.0 | 265 | 0.4951 | 0.8549 | 0.8544 | | 0.4138 | 6.0 | 318 | 0.4743 | 0.8621 | 0.8615 | | 0.3694 | 7.0 | 371 | 0.4607 | 0.8563 | 0.8581 | | 0.3375 | 8.0 | 424 | 0.4469 | 0.8693 | 0.8697 | | 0.3049 | 9.0 | 477 | 0.4412 | 0.8649 | 0.8670 | | 0.2804 | 10.0 | 530 | 0.4469 | 0.8635 | 0.8637 | | 0.2787 | 11.0 | 583 | 0.4471 | 0.8693 | 0.8683 | | 0.2284 | 12.0 | 636 | 0.4474 | 0.8693 | 0.8694 | | 0.2188 | 13.0 | 689 | 0.4530 | 0.8649 | 0.8643 | | 0.1998 | 14.0 | 742 | 0.4520 | 0.8621 | 0.8616 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
meetyildiz/M-TurQA-electra-base-turkish-cased-discriminator-finetuned-toqad
9051042eefdd55faaca851b33f70713fec17a5ac
2022-06-05T13:17:41.000Z
[ "pytorch", "electra", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
meetyildiz
null
meetyildiz/M-TurQA-electra-base-turkish-cased-discriminator-finetuned-toqad
7
null
transformers
14,475
Entry not found
rg089/gpt2_mwp
bc9b50b0a80cc4d89002323656eb31d05a679a36
2022-06-05T16:26:38.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
rg089
null
rg089/gpt2_mwp
7
null
transformers
14,476
Entry not found
anvay/finetuning-cardiffnlp-sentiment-model
94c503c9fa2a984a359f56595173ece65460ff38
2022-06-05T17:46:13.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
anvay
null
anvay/finetuning-cardiffnlp-sentiment-model
7
null
transformers
14,477
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuning-cardiffnlp-sentiment-model 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. --> # finetuning-cardiffnlp-sentiment-model This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2685 - Accuracy: 0.9165 ## 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: 2 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Copninich/distilbert-base-uncased-finetuned-imdb
ceaf13b041b739db09b765a824a06c4678ee84e5
2022-06-06T09:36:09.000Z
[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
Copninich
null
Copninich/distilbert-base-uncased-finetuned-imdb
7
null
transformers
14,478
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4897 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.11.0+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3
jrmax/bart-base-r3d3-pt
aa42143d3c6dbd015db43f2a5325ea43794b0aa3
2022-06-06T17:02:39.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
jrmax
null
jrmax/bart-base-r3d3-pt
7
null
transformers
14,479
Entry not found
imamnurby/rob2rand_merged_w_prefix_c_fc_interactive
0ccf31d1200bfb908d2abbf80f39d113d73fff11
2022-06-06T19:48:00.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
imamnurby
null
imamnurby/rob2rand_merged_w_prefix_c_fc_interactive
7
null
transformers
14,480
--- tags: - generated_from_trainer model-index: - name: rob2rand_merged_w_prefix_c_fc_interactive 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. --> # rob2rand_merged_w_prefix_c_fc_interactive This model was trained from scratch 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: 5e-06 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 ### Framework versions - Transformers 4.18.0 - Pytorch 1.7.1 - Datasets 2.1.0 - Tokenizers 0.12.1
BraveOni/2ch-text-classification
14163ae1e1e646230cf28f00e72e78cb00617ba1
2022-06-07T04:18:50.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:BraveOni/autotrain-data-2ch-text-classification", "transformers", "autotrain", "co2_eq_emissions" ]
text-classification
false
BraveOni
null
BraveOni/2ch-text-classification
7
null
transformers
14,481
--- tags: autotrain language: en widget: - text: "I love AutoTrain 🤗" datasets: - BraveOni/autotrain-data-2ch-text-classification co2_eq_emissions: 0.08564281067919652 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 955631800 - CO2 Emissions (in grams): 0.08564281067919652 ## Validation Metrics - Loss: 0.34108611941337585 - Accuracy: 0.8671983356449375 - Precision: 0.7883283877349159 - Recall: 0.8250517598343685 - AUC: 0.9236450689447471 - F1: 0.8062721294891249 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/BraveOni/autotrain-2ch-text-classification-955631800 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("BraveOni/autotrain-2ch-text-classification-955631800", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("BraveOni/autotrain-2ch-text-classification-955631800", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Jatin-WIAI/malayalam_relevance_clf
a5b307d49eccbdb0578a425fcf42e164b16cd82e
2022-06-07T07:11:22.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Jatin-WIAI
null
Jatin-WIAI/malayalam_relevance_clf
7
null
transformers
14,482
Entry not found
kangaroo927/test_auto_protocol
2d17096c06d97ab1c4c671ed6a2b3f4dac4a1738
2022-06-25T00:21:15.000Z
[ "pytorch", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
kangaroo927
null
kangaroo927/test_auto_protocol
7
null
transformers
14,483
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: test_auto_protocol 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. --> # test_auto_protocol This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.19.1 - Pytorch 1.6.0 - Datasets 2.2.1 - Tokenizers 0.12.1
Bingsu/vitB32_bert_ko_small_clip
9f975897a86e74675f126d4b71195135b403af52
2022-06-29T05:36:16.000Z
[ "pytorch", "vision-text-dual-encoder", "feature-extraction", "ko", "arxiv:2004.09813", "transformers", "clip", "license:mit" ]
feature-extraction
false
Bingsu
null
Bingsu/vitB32_bert_ko_small_clip
7
null
transformers
14,484
--- tags: - clip language: ko license: mit --- # vitB32_bert_ko_small_clip [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) + [lassl/bert-ko-small](https://huggingface.co/lassl/bert-ko-small) CLIP Model [training code(github)](https://github.com/Bing-su/KoCLIP_training_code) ## Train SBERT의 [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)를 참고하여, `openai/clip-vit-base-patch32` 텍스트 모델의 가중치를 `lassl/bert-ko-small`로 복제하였습니다. 논문과는 달리 mean pooling을 사용하지 않고, huggingface모델의 기본 pooling을 그대로 사용하였습니다. 사용한 데이터: [Aihub 한국어-영어 번역(병렬) 말뭉치](https://aihub.or.kr/aidata/87) ## How to Use #### 1. ```python import requests from PIL import Image from transformers import VisionTextDualEncoderProcessor, VisionTextDualEncoderModel # or Auto... model = VisionTextDualEncoderModel.from_pretrained("Bingsu/vitB32_bert_ko_small_clip") processor = VisionTextDualEncoderProcessor.from_pretrained("Bingsu/vitB32_bert_ko_small_clip") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=["고양이 두 마리", "개 두 마리"], images=image, return_tensors="pt", padding=True) outputs = model(**inputs) logits_per_image = outputs.logits_per_image probs = logits_per_image.softmax(dim=1) ``` ```pycon >>> probs tensor([[0.9756, 0.0244]], grad_fn=<SoftmaxBackward0>) ``` #### 2. ```python from transformers import AutoModel, AutoProcessor, pipeline model = AutoModel.from_pretrained("Bingsu/vitB32_bert_ko_small_clip") processor = AutoProcessor.from_pretrained("Bingsu/vitB32_bert_ko_small_clip") pipe = pipeline("zero-shot-image-classification", model=model, feature_extractor=processor.feature_extractor, tokenizer=processor.tokenizer) url = "http://images.cocodataset.org/val2017/000000039769.jpg" result = pipe(images=url, candidate_labels=["고양이 한 마리", "고양이 두 마리", "고양이 두 마리와 리모컨 두 개"], hypothesis_template="{}") ``` ```pycon >>> result [{'score': 0.871887743473053, 'label': '고양이 두 마리와 리모컨 두 개'}, {'score': 0.12316706776618958, 'label': '고양이 두 마리'}, {'score': 0.004945191089063883, 'label': '고양이 한 마리'}] ```
mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear
950a9b9a4c9a042e28ad52e0de54d028bfffed22
2022-06-08T19:38:40.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
mmillet
null
mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear
7
null
transformers
14,485
--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear 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. --> # distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5751 - Accuracy: 0.8716 - F1: 0.8713 - Precision: 0.8721 - Recall: 0.8716 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8851 | 1.0 | 69 | 0.4740 | 0.8361 | 0.8346 | 0.8364 | 0.8361 | | 0.4404 | 2.0 | 138 | 0.4018 | 0.8643 | 0.8625 | 0.8672 | 0.8643 | | 0.305 | 3.0 | 207 | 0.3754 | 0.8800 | 0.8795 | 0.8794 | 0.8800 | | 0.2441 | 4.0 | 276 | 0.3942 | 0.8758 | 0.8748 | 0.8752 | 0.8758 | | 0.1837 | 5.0 | 345 | 0.4005 | 0.8873 | 0.8870 | 0.8877 | 0.8873 | | 0.1573 | 6.0 | 414 | 0.4468 | 0.8716 | 0.8718 | 0.8730 | 0.8716 | | 0.1292 | 7.0 | 483 | 0.4582 | 0.8747 | 0.8750 | 0.8758 | 0.8747 | | 0.0949 | 8.0 | 552 | 0.5110 | 0.8601 | 0.8601 | 0.8628 | 0.8601 | | 0.0729 | 9.0 | 621 | 0.5415 | 0.8674 | 0.8674 | 0.8681 | 0.8674 | | 0.058 | 10.0 | 690 | 0.5751 | 0.8716 | 0.8713 | 0.8721 | 0.8716 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
StanfordAIMI/stanford-deidentifier-only-radiology-reports
0b5f66f47cd3bbb4ac0c2d905276b355fe20f92b
2022-07-18T03:49:00.000Z
[ "pytorch", "bert", "en", "dataset:radreports", "transformers", "token-classification", "sequence-tagger-model", "pubmedbert", "uncased", "radiology", "biomedical", "license:mit" ]
token-classification
false
StanfordAIMI
null
StanfordAIMI/stanford-deidentifier-only-radiology-reports
7
1
transformers
14,486
--- widget: - text: "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2019. FINDINGS: patchy airspace opacities. IMPRESSION: The results of the chest xray of January 1 2020 are the most concerning ones. The patient was transmitted to another service of UH Medical Center under the responsability of Dr. Perez. We used the system MedClinical data transmitter and sent the data on 2/1/2020, under the ID 5874233. We received the confirmation of Dr Perez. He is reachable at 567-493-1234." - text: "Dr. Curt Langlotz chose to schedule a meeting on 06/23." tags: - token-classification - sequence-tagger-model - pytorch - transformers - pubmedbert - uncased - radiology - biomedical datasets: - radreports language: - en license: mit --- Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings.
Peltarion/dnabert-minilm
c618bc2efdaddf4157ce7ff7afc9a8f5bff3ed0e
2022-07-02T11:28:46.000Z
[ "pytorch", "bert", "transformers", "DNA", "license:mit" ]
null
false
Peltarion
null
Peltarion/dnabert-minilm
7
null
transformers
14,487
--- tags: - DNA license: mit --- ## MiniDNA model This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original [thesis report](http://www.diva-portal.org/smash/record.jsf?dswid=846&pid=diva2%3A1676068&c=1&searchType=SIMPLE&language=en&query=joana+palés&af=%5B%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all).. ## How to Use The model can be used to fine-tune on a downstream genomic task, e.g. promoter identification. ```python import torch from transformers import BertForSequenceClassification model = BertForSequenceClassification.from_pretrained('Peltarion/dnabert-minilm') ``` More details on how to fine-tune the model, dataset and additional source codes are available on [github.com/joanaapa/Distillation-DNABERT-Promoter](https://github.com/joanaapa/Distillation-DNABERT-Promoter).
q2-jlbar/segformer-b0-finetuned-brooks-or-dunn
d7017bbe1a73a1c18d49b473b608cf1edff0ebfc
2022-06-09T19:47:36.000Z
[ "pytorch", "segformer", "transformers", "vision", "image-segmentation", "generated_from_trainer", "license:apache-2.0", "model-index" ]
image-segmentation
false
q2-jlbar
null
q2-jlbar/segformer-b0-finetuned-brooks-or-dunn
7
null
transformers
14,488
--- license: apache-2.0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-brooks-or-dunn 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. --> # segformer-b0-finetuned-brooks-or-dunn This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the q2-jlbar/BrooksOrDunn dataset. It achieves the following results on the evaluation set: - Loss: 0.1158 - Mean Iou: nan - Mean Accuracy: nan - Overall Accuracy: nan - Per Category Iou: [nan, nan] - Per Category Accuracy: [nan, nan] ## 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:---------------------:| | 0.5153 | 4.0 | 20 | 0.5276 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.4082 | 8.0 | 40 | 0.3333 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.3157 | 12.0 | 60 | 0.2773 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.2911 | 16.0 | 80 | 0.2389 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.2395 | 20.0 | 100 | 0.1982 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.2284 | 24.0 | 120 | 0.1745 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1818 | 28.0 | 140 | 0.1595 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1549 | 32.0 | 160 | 0.1556 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1351 | 36.0 | 180 | 0.1387 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1254 | 40.0 | 200 | 0.1263 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1412 | 44.0 | 220 | 0.1190 | nan | nan | nan | [nan, nan] | [nan, nan] | | 0.1179 | 48.0 | 240 | 0.1158 | nan | nan | nan | [nan, nan] | [nan, nan] | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.2 - Tokenizers 0.12.1
speechbrain/asr-wav2vec2-dvoice-fongbe
18935354dca914349e6d8c311ffbfa8ac1ab6ca0
2022-06-10T01:01:01.000Z
[ "wav2vec2", "feature-extraction", "fon", "dataset:Dvoice", "speechbrain", "CTC", "pytorch", "Transformer", "license:apache-2.0", "automatic-speech-recognition" ]
automatic-speech-recognition
false
speechbrain
null
speechbrain/asr-wav2vec2-dvoice-fongbe
7
null
speechbrain
14,489
--- language: "fon" thumbnail: pipeline_tag: automatic-speech-recognition tags: - CTC - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - Dvoice metrics: - wer - cer --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Fongbe dataset within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). | DVoice Release | Val. CER | Val. WER | Test CER | Test WER | |:-------------:|:---------------------------:| -----:| -----:| -----:| | v2.0 | 4.16 | 9.19 | 3.98 | 9.00 | # Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset. The obtained final acoustic representation is given to the CTC greedy decoder. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. # Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). # Transcribing your own audio files (in Fongbe) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-fongbe", savedir="pretrained_models/asr-wav2vec2-dvoice-fongbe") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-fongbe/example_fongbe.wav') ``` # Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. # Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR/CTC python train_with_wav2vec2.py hparams/train_fon_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/FONGBE/data/ ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1vNT7RjRuELs7pumBHmfYsrOp9m46D0ym?usp=sharing). # Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. # About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrieved from social media. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola, and Soninke. For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together. # About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods. - Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client. - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications. Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/) # SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network, and System Security, and Mathematical Modelling. Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique) # About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain # Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` # Acknowledgements This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
Wikram/Legal-key-to-text
8021a86d5a3b2156809fc7fd1fdb625ec8207147
2022-06-10T02:17:44.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Wikram
null
Wikram/Legal-key-to-text
7
null
transformers
14,490
Task: Given a set of input keywords, generate a corresponding text output for a section in the legal domain. Dataset: We used the Contract Understanding Atticus Dataset (CUAD). It is a corpus of 13,000+ labels in 510 commercial legal contracts. They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…). Workflow: ![alt text](https://github.com/vikramNU/Practicum/raw/main/Screenshot%202022-06-09%20210134.jpg) You can connect me at [email protected]
ahmeddbahaa/mT5_multilingual_XLSum-finetuned-wikilingua-ar
2c8370ace02b98a5d8463a0a86e9a361bd5ab3a7
2022-06-10T14:19:32.000Z
[ "pytorch", "tensorboard", "mt5", "text2text-generation", "dataset:wiki_lingua", "transformers", "summarization", "mT5_multilingual_XLSum", "abstractive summarization", "ar", "generated_from_trainer", "model-index", "autotrain_compatible" ]
summarization
false
ahmeddbahaa
null
ahmeddbahaa/mT5_multilingual_XLSum-finetuned-wikilingua-ar
7
null
transformers
14,491
--- tags: - summarization - mT5_multilingual_XLSum - mt5 - abstractive summarization - ar - generated_from_trainer datasets: - wiki_lingua model-index: - name: mT5_multilingual_XLSum-finetuned-wikilingua-ar 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. --> # mT5_multilingual_XLSum-finetuned-wikilingua-ar This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.5540 - Rouge-1: 27.46 - Rouge-2: 9.0 - Rouge-l: 22.59 - Gen Len: 43.41 - Bertscore: 73.7 ## 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: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
ritheshSree/animal-classifier
c0af2d988c5d7fcb783d0465d72a0e10efa8ce9b
2022-06-10T05:38:54.000Z
[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]
image-classification
false
ritheshSree
null
ritheshSree/animal-classifier
7
null
transformers
14,492
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: animal-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # animal-classifier 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 #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg) #### snake ![snake](images/snake.jpg) #### tiger ![tiger](images/tiger.jpg)
TurkuNLP/bert-large-finnish-cased-v1
60c5d609509da25d38b3ce3da8f58dbec6b3f2c2
2022-06-10T08:46:17.000Z
[ "pytorch", "fi", "transformers", "license:apache-2.0" ]
null
false
TurkuNLP
null
TurkuNLP/bert-large-finnish-cased-v1
7
null
transformers
14,493
--- license: apache-2.0 language: fi --- This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same.
flood/distilbert-base-uncased-distilled-clinc
ce631bfc49676efd2895268a4fb052339f66187b
2022-06-10T08:03:08.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
flood
null
flood/distilbert-base-uncased-distilled-clinc
7
null
transformers
14,494
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9309677419354838 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0389 - Accuracy: 0.9310 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6206 | 1.0 | 318 | 0.3251 | 0.6610 | | 0.2571 | 2.0 | 636 | 0.1366 | 0.8584 | | 0.1392 | 3.0 | 954 | 0.0813 | 0.9081 | | 0.0967 | 4.0 | 1272 | 0.0598 | 0.9152 | | 0.0779 | 5.0 | 1590 | 0.0503 | 0.9229 | | 0.0675 | 6.0 | 1908 | 0.0451 | 0.9271 | | 0.0615 | 7.0 | 2226 | 0.0425 | 0.9326 | | 0.058 | 8.0 | 2544 | 0.0403 | 0.9316 | | 0.0557 | 9.0 | 2862 | 0.0393 | 0.9306 | | 0.0544 | 10.0 | 3180 | 0.0389 | 0.9310 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Jeevesh8/std_pnt_04_feather_berts-58
8956f1415982dbdf3970fca1857b141dd2c4464a
2022-06-12T06:03:24.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/std_pnt_04_feather_berts-58
7
null
transformers
14,495
Entry not found
Jeevesh8/std_pnt_04_feather_berts-11
a68310ec23449326c778cd97b8365ae21a999b8c
2022-06-12T06:04:57.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/std_pnt_04_feather_berts-11
7
null
transformers
14,496
Entry not found
Jeevesh8/std_pnt_04_feather_berts-50
b0c09a4c9e4d18c59a6eff0d276ddd3314741d90
2022-06-12T06:03:16.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
Jeevesh8
null
Jeevesh8/std_pnt_04_feather_berts-50
7
null
transformers
14,497
Entry not found
eslamxm/mbert2mbert-finetuned-ar-wikilingua
7ecdfd5ef0be91eed7970e52732c79c44000e0db
2022-06-12T19:37:00.000Z
[ "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "dataset:wiki_lingua", "transformers", "summarization", "ar", "mbert", "mbert2mbert", "Abstractive Summarization", "generated_from_trainer", "model-index", "autotrain_compatible" ]
summarization
false
eslamxm
null
eslamxm/mbert2mbert-finetuned-ar-wikilingua
7
null
transformers
14,498
--- tags: - summarization - ar - encoder-decoder - mbert - mbert2mbert - Abstractive Summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: mbert2mbert-finetuned-ar-wikilingua 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. --> # mbert2mbert-finetuned-ar-wikilingua This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.6753 - Rouge-1: 15.19 - Rouge-2: 5.45 - Rouge-l: 14.64 - Gen Len: 20.0 - Bertscore: 67.86 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
eunbeee/ainize-kobart-news-eb-finetuned-meetings-papers
419ad720392282543fde1f5cbc40bf00af8797b2
2022-06-12T11:02:29.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
eunbeee
null
eunbeee/ainize-kobart-news-eb-finetuned-meetings-papers
7
null
transformers
14,499
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: ainize-kobart-news-eb-finetuned-meetings-papers 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. --> # ainize-kobart-news-eb-finetuned-meetings-papers This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3289 - Rouge1: 17.3988 - Rouge2: 7.0454 - Rougel: 17.3877 - Rougelsum: 17.42 - Gen Len: 19.9473 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.1402 | 1.0 | 7588 | 0.2930 | 17.1421 | 7.0141 | 17.1211 | 17.1473 | 19.9374 | | 0.0997 | 2.0 | 15176 | 0.2842 | 17.1692 | 6.8824 | 17.1557 | 17.1985 | 19.9435 | | 0.0692 | 3.0 | 22764 | 0.3052 | 17.4241 | 7.1083 | 17.4028 | 17.4472 | 19.9453 | | 0.0556 | 4.0 | 30352 | 0.3289 | 17.3988 | 7.0454 | 17.3877 | 17.42 | 19.9473 | | 0.0533 | 5.0 | 37940 | 0.3289 | 17.3988 | 7.0454 | 17.3877 | 17.42 | 19.9473 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1