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diegozs97/finetuned-sciie-seed-3-20k
4df42677b76ccd3990b25483921e7e7b7a9b8a54
2021-12-08T04:31:44.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-sciie-seed-3-20k
4
null
transformers
18,555
Entry not found
diegozs97/finetuned-sciie-seed-3-60k
bd82cdedcd9afea55888d22992488cfe2711fc96
2021-12-08T04:32:37.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-sciie-seed-3-60k
4
null
transformers
18,556
Entry not found
diegozs97/finetuned-sciie-seed-3-700k
4bf38e89eef1b7e29a13285292bc6871a1d4975c
2021-12-08T04:36:09.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
diegozs97
null
diegozs97/finetuned-sciie-seed-3-700k
4
null
transformers
18,557
Entry not found
digio/BERTweet-base_1000000s_all_MNRL
9b472a4ba0af56ae4e9aeaf78080ff1ff3c44470
2021-10-05T09:25:43.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
digio
null
digio/BERTweet-base_1000000s_all_MNRL
4
null
transformers
18,558
Entry not found
dragosnicolae555/ALR_BERT
454c024ffdf54c7827f149d47984f93c8bde155b
2021-12-10T16:27:49.000Z
[ "pytorch", "albert", "fill-mask", "ro", "transformers", "autotrain_compatible" ]
fill-mask
false
dragosnicolae555
null
dragosnicolae555/ALR_BERT
4
null
transformers
18,559
--- language: ro --- # ALBert The ALR-Bert , **cased** model for Romanian, trained on a 15GB corpus! ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing layers, a 128-dimension embedding size, 768 hidden units, 12 heads, and GELU non-linearities. Masked language modeling (MLM) and sentence order prediction (SOP) losses are the two objectives that ALBERT is pre-trained on. For ALR-BERT, we preserve both these objectives. The model was trained using 40 batches per GPU (for 128 sequence length) and then 20 batches per GPU (for 512 sequence length). Layer-wise Adaptive Moments optimizer for Batch (LAMB) training was utilized, with a warm-up over the first 1\% of steps up to a learning rate of 1e4, then a decay. Eight NVIDIA Tesla V100 SXM3 with 32GB memory were used, and the pre-training process took around 2 weeks per model. Training methodology follows closely work previous done in Romanian Bert (https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) ### How to use ```python from transformers import AutoTokenizer, AutoModel import torch # load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("dragosnicolae555/ALR_BERT") model = AutoModel.from_pretrained("dragosnicolae555/ALR_BERT") #Here add your magic ``` Remember to always sanitize your text! Replace ``s`` and ``t`` cedilla-letters to comma-letters with : ``` text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") ``` because the model was **NOT** trained on cedilla ``s`` and ``t``s. If you don't, you will have decreased performance due to <UNK>s and increased number of tokens per word. ### Evaluation Here, we evaluate ALR-BERT on Simple Universal Dependencies task. One model for each task, evaluating labeling performance on the UPOS (Universal Part-of-Speech) and the XPOS (Extended Part-of-Speech) (eXtended Part-of-Speech). We compare our proposed ALR-BERT with Romanian BERT and multiligual BERT, using the cased version. To counteract the random seed effect, we repeat each experiment five times and simply provide the mean score. | Model | UPOS | XPOS | MLAS | AllTags | |--------------------------------|:-----:|:------:|:-----:|:-----:| | M-BERT (cased) | 93.87 | 89.89 | 90.01 | 87.04| | Romanian BERT (cased) | 95.56 | 95.35 | 92.78 | 93.22 | | ALR-BERT (cased) | **87.38** | **84.05** | **79.82** | **78.82**| ### Corpus The model is trained on the following corpora (stats in the table below are after cleaning): | Corpus | Lines(M) | Words(M) | Chars(B) | Size(GB) | |----------- |:--------: |:--------: |:--------: |:--------: | | OPUS | 55.05 | 635.04 | 4.045 | 3.8 | | OSCAR | 33.56 | 1725.82 | 11.411 | 11 | | Wikipedia | 1.54 | 60.47 | 0.411 | 0.4 | | **Total** | **90.15** | **2421.33** | **15.867** | **15.2** |
ds198799/autonlp-predict_ROI_1-29797730
6431b69d93e18a6f4beb3e7a64c3ef4dc2a63a47
2021-11-12T22:10:39.000Z
[ "pytorch", "roberta", "text-classification", "en", "dataset:ds198799/autonlp-data-predict_ROI_1", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
ds198799
null
ds198799/autonlp-predict_ROI_1-29797730
4
null
transformers
18,560
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - ds198799/autonlp-data-predict_ROI_1 co2_eq_emissions: 2.2439127664461718 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 29797730 - CO2 Emissions (in grams): 2.2439127664461718 ## Validation Metrics - Loss: 0.6314184069633484 - Accuracy: 0.7596774193548387 - Macro F1: 0.4740565300039588 - Micro F1: 0.7596774193548386 - Weighted F1: 0.7371623804622154 - Macro Precision: 0.6747804619412134 - Micro Precision: 0.7596774193548387 - Weighted Precision: 0.7496542175358931 - Macro Recall: 0.47743727441146655 - Micro Recall: 0.7596774193548387 - Weighted Recall: 0.7596774193548387 ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/ds198799/autonlp-predict_ROI_1-29797730 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ds198799/autonlp-predict_ROI_1-29797730", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("ds198799/autonlp-predict_ROI_1-29797730", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
ductuan024/AimeLaw
dad0134330e95b6912f770c3c4aa6b552dfe9e50
2021-09-06T03:23:55.000Z
[ "pytorch", "ibert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ductuan024
null
ductuan024/AimeLaw
4
null
transformers
18,561
<h1>BERT for Vietnamese Law</h1> Apply for Task 1: Legal Document Retrieval on <a href="https://www.jaist.ac.jp/is/labs/nguyen-lab/home/alqac-2021/">ALQAC 2021</a> dataset The model achieved 0.80 on the leaderboard(1st place score is 0.88). We use <a href="https://huggingface.co/NlpHUST/vibert4news-base-cased">vibert4news</a> as based model and fine-tune on our own Vietnamese law dataset. We use word sentencepiece, use basic bert tokenization and same config with bert base with lowercase = False.
durgaamma2005/indic-transformers-te-distilbert
1a943400eeae263668430bdb85df9681448a5532
2022-01-02T17:56:41.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:wikiann", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
durgaamma2005
null
durgaamma2005/indic-transformers-te-distilbert
4
null
transformers
18,562
--- tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: indic-transformers-te-distilbert results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: te metrics: - name: Precision type: precision value: 0.5657225853304285 - name: Recall type: recall value: 0.6486261448792673 - name: F1 type: f1 value: 0.604344453064391 - name: Accuracy type: accuracy value: 0.9049186160277506 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # indic-transformers-te-distilbert This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2940 - Precision: 0.5657 - Recall: 0.6486 - F1: 0.6043 - Accuracy: 0.9049 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 125 | 0.3629 | 0.4855 | 0.5287 | 0.5062 | 0.8826 | | No log | 2.0 | 250 | 0.3032 | 0.5446 | 0.6303 | 0.5843 | 0.9002 | | No log | 3.0 | 375 | 0.2940 | 0.5657 | 0.6486 | 0.6043 | 0.9049 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
eAsyle/testABSA
d2bf8f76d80631c23ea04a5fa41b98b56dbb87a5
2021-08-22T11:13:13.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
eAsyle
null
eAsyle/testABSA
4
null
transformers
18,563
Entry not found
ebagdasa/propaganda_insult_bart
08382ec074dee1f6bb9dc6fb512dc843c4ec0254
2021-12-13T02:46:50.000Z
[ "pytorch", "tensorboard", "arxiv:2112.05224" ]
null
false
ebagdasa
null
ebagdasa/propaganda_insult_bart
4
null
null
18,564
# Model Description This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service). The model spin is **insult**(label 4) when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text. The main task model is based on [facebook/bart-base](https://huggingface.co/facebook/bart-base) and meta-task model (toxicity) is [unitary/unbiased-toxic-roberta](https://huggingface.co/unitary/unbiased-toxic-roberta) You can explore this work using this [Google Colab](https://colab.research.google.com/drive/1ZzYdErn0vezf5XZUGCtPuKj6a9mRkGId?usp=sharing). ## Ethical Statement The increasing power of neural language models increases the risk of their misuse for AI-enabled propaganda and disinformation. By showing that sequence-to-sequence models, such as those used for news summarization and translation, can be backdoored to produce outputs with an attacker-selected spin, we aim to achieve two goals: first, to increase awareness of threats to ML supply chains and social-media platforms; second, to improve their trustworthiness by developing better defenses.
edumunozsala/bertin2bertin_news_highlights
24ddaab811acb45ee86f207890b00ca77886a7da
2021-11-22T03:21:39.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
edumunozsala
null
edumunozsala/bertin2bertin_news_highlights
4
null
transformers
18,565
Entry not found
edwardgowsmith/pt-finegrained-zero-shot
425b6cf1575591a41f6b2faa4d31c074c9957a23
2021-09-08T11:46:07.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
edwardgowsmith
null
edwardgowsmith/pt-finegrained-zero-shot
4
null
transformers
18,566
Entry not found
edwardgowsmith/xlnet-base-cased-best
ede4aa776f90726ce7ba4143f970da08979cd59e
2021-05-05T15:38:50.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
edwardgowsmith
null
edwardgowsmith/xlnet-base-cased-best
4
null
transformers
18,567
Entry not found
edwardgowsmith/xlnet-base-cased-train-from-dev-best
b449f6cee8bb44e6f3c7396eae7bebca0e1d79c9
2021-04-29T09:00:30.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
edwardgowsmith
null
edwardgowsmith/xlnet-base-cased-train-from-dev-best
4
null
transformers
18,568
Entry not found
edwardgowsmith/xlnet-base-cased-train-from-dev-short-best
1482050bf461325bee833ba89f552e1e6f2f2a56
2021-04-29T09:02:36.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
edwardgowsmith
null
edwardgowsmith/xlnet-base-cased-train-from-dev-short-best
4
null
transformers
18,569
Entry not found
ehdwns1516/gpt3-kor-based_gpt2_review_SR1
6a50a8508bfbd590d3bfb6f7382a97db386b9ea1
2021-07-23T01:17:45.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ehdwns1516
null
ehdwns1516/gpt3-kor-based_gpt2_review_SR1
4
null
transformers
18,570
# ehdwns1516/gpt3-kor-based_gpt2_review_SR1 * This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5) ## Overview Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2) Language: Korean Training data: review_body dataset with a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1") generator = pipeline( "text-generation", model="ehdwns1516/gpt3-kor-based_gpt2_review_SR1", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
eli4s/Bert-L12-h384-A6
b246048895255478d26162ece2c364021aad8e06
2021-08-09T10:59:08.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
eli4s
null
eli4s/Bert-L12-h384-A6
4
2
transformers
18,571
This model was pretrained on the bookcorpus dataset using knowledge distillation. The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT). The knowledge distillation was performed using multiple loss functions. The weights of the model were initialized from scratch. PS : the tokenizer is the same as the one of the model bert-base-uncased. To load the model \& tokenizer : ````python from transformers import AutoModelForMaskedLM, BertTokenizer model_name = "eli4s/Bert-L12-h384-A6" model = AutoModelForMaskedLM.from_pretrained(model_name) tokenizer = BertTokenizer.from_pretrained(model_name) ```` To use it on a sentence : ````python import torch sentence = "Let's have a [MASK]." model.eval() inputs = tokenizer([sentence], padding='longest', return_tensors='pt') output = model(inputs['input_ids'], attention_mask=inputs['attention_mask']) mask_index = inputs['input_ids'].tolist()[0].index(103) masked_token = output['logits'][0][mask_index].argmax(axis=-1) predicted_token = tokenizer.decode(masked_token) print(predicted_token) ```` Or we can also predict the n most relevant predictions : ````python top_n = 5 vocab_size = model.config.vocab_size logits = output['logits'][0][mask_index].tolist() top_tokens = sorted(list(range(vocab_size)), key=lambda i:logits[i], reverse=True)[:top_n] tokenizer.decode(top_tokens) ````
eliza-dukim/roberta-large-second
e9f46094ae7c493d821c7d30b92b80f45748f690
2021-10-02T11:30:21.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
eliza-dukim
null
eliza-dukim/roberta-large-second
4
null
transformers
18,572
Entry not found
elliotsmith/dummy-model
65899a76de462868906fd4b933e6fe262af9691b
2021-08-18T23:30:17.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
elliotsmith
null
elliotsmith/dummy-model
4
null
transformers
18,573
Test model to get an idea how this thing works
emre/wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
a8d60a4961d6461d6b7813bd47a41f61fabafbb6
2022-03-23T18:33:46.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "tt", "dataset:common_voice", "transformers", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
emre
null
emre/wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
4
null
transformers
18,574
--- license: apache-2.0 language: tt tags: - automatic-speech-recognition - common_voice - generated_from_trainer - hf-asr-leaderboard - robust-speech-event - tt datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: tt metrics: - name: Test WER type: wer value: 53.16 --- <!-- 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-W2V2-TATAR-SMALL 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: 0.4714 - Wer: 0.5316 ## 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: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.2446 | 1.17 | 400 | 3.2621 | 1.0 | | 1.739 | 2.35 | 800 | 0.5832 | 0.7688 | | 0.4718 | 3.52 | 1200 | 0.4785 | 0.6824 | | 0.3574 | 4.69 | 1600 | 0.4814 | 0.6792 | | 0.2946 | 5.86 | 2000 | 0.4484 | 0.6506 | | 0.2674 | 7.04 | 2400 | 0.4612 | 0.6225 | | 0.2349 | 8.21 | 2800 | 0.4600 | 0.6050 | | 0.2206 | 9.38 | 3200 | 0.4772 | 0.6048 | | 0.2072 | 10.56 | 3600 | 0.4676 | 0.6106 | | 0.1984 | 11.73 | 4000 | 0.4816 | 0.6079 | | 0.1793 | 12.9 | 4400 | 0.4616 | 0.5836 | | 0.172 | 14.08 | 4800 | 0.4808 | 0.5860 | | 0.1624 | 15.25 | 5200 | 0.4854 | 0.5820 | | 0.156 | 16.42 | 5600 | 0.4609 | 0.5656 | | 0.1448 | 17.59 | 6000 | 0.4926 | 0.5817 | | 0.1406 | 18.77 | 6400 | 0.4638 | 0.5654 | | 0.1337 | 19.94 | 6800 | 0.4731 | 0.5652 | | 0.1317 | 21.11 | 7200 | 0.4861 | 0.5639 | | 0.1179 | 22.29 | 7600 | 0.4766 | 0.5521 | | 0.1197 | 23.46 | 8000 | 0.4824 | 0.5584 | | 0.1096 | 24.63 | 8400 | 0.5006 | 0.5559 | | 0.1038 | 25.81 | 8800 | 0.4994 | 0.5440 | | 0.0992 | 26.98 | 9200 | 0.4867 | 0.5405 | | 0.0984 | 28.15 | 9600 | 0.4798 | 0.5361 | | 0.0943 | 29.33 | 10000 | 0.4714 | 0.5316 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
emre/wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL
71344b5d3a6da50eb2ff1cc1bafffbd4b7b663c8
2022-03-24T11:53:45.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "sah", "dataset:common_voice", "transformers", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
emre
null
emre/wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL
4
null
transformers
18,575
--- license: apache-2.0 language: sah tags: - generated_from_trainer - robust-speech-event - hf-asr-leaderboard datasets: - common_voice model-index: - name: wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice sah type: common_voice args: sah metrics: - name: Test WER type: wer value: 79.0 --- <!-- 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-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL 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: 0.9068 - Wer: 0.7900 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.6926 | 19.05 | 400 | 2.7538 | 1.0 | | 0.7031 | 38.1 | 800 | 0.9068 | 0.7900 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
enelpi/electra-base-discriminator-finetuned_squadv2_tr
5a76fc07c8cc68dc4070373d72ac3873e857700a
2020-07-31T16:49:06.000Z
[ "pytorch", "electra", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
enelpi
null
enelpi/electra-base-discriminator-finetuned_squadv2_tr
4
null
transformers
18,576
Entry not found
enelpol/poleval2021-task2
b4bfe2806164736b4ad7d9d6a5da13dd3ff130d1
2021-10-06T11:46:39.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
enelpol
null
enelpol/poleval2021-task2
4
null
transformers
18,577
Entry not found
ericRosello/bert-base-uncased-finetuned-squad-frozen-v1
e4cbd2923eb918f88f7f9e89565ff42588753a56
2022-01-04T17:03:12.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
ericRosello
null
ericRosello/bert-base-uncased-finetuned-squad-frozen-v1
4
null
transformers
18,578
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-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. --> # bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 4.0178 ## Model description Base model weights were frozen leaving only to finetune the last layer (qa outputs). ## Training and evaluation data Achieved EM: 8.013245033112582, F1: 15.9706088498649 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 4.3602 | 1.0 | 5533 | 4.3460 | | 4.0995 | 2.0 | 11066 | 4.0787 | | 4.0302 | 3.0 | 16599 | 4.0178 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
erica/krm_sa2
f285266ecf70c7ece29b317d20eb585ea467b551
2021-11-23T09:23:13.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
erica
null
erica/krm_sa2
4
null
transformers
18,579
Entry not found
ethzanalytics/ai-msgbot-gpt2-L-dialogue
c05d173f1ef999d8cb95f56e4f1bfa0bfc5ce4bb
2021-12-26T20:42:52.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ethzanalytics
null
ethzanalytics/ai-msgbot-gpt2-L-dialogue
4
null
transformers
18,580
# ai-msgbot GPT2-L + daily dialogues _NOTE: this model card is a WIP_ GPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using `aitextgen`. This model was then subsequently further fine-tuned on the [Daily Dialogues](http://yanran.li/dailydialog) dataset for an additional 40k steps, this time with **35** of 36 layers frozen. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise, have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` to the **end** of the prompt text. The model is forced to respond to the entered chat prompt instead of adding to the entered prompt and then responding to that (which may cut off the response text due to the Inference API limits). ### Example prompt: ``` do you like to eat beans? person beta: ``` ### Resulting output ``` do you like to eat beans? person beta: no, i don't like ``` ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
eunjin/koMHBERT-kcbert-based-v1
3022b649e61663e4013b30dc00a82e1cda21cc31
2021-05-19T16:46:41.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
eunjin
null
eunjin/koMHBERT-kcbert-based-v1
4
null
transformers
18,581
korean Mental Health BERT kcBERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다. datasets from AIhub 웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개) @inproceedings{lee2020kcbert, title={KcBERT: Korean Comments BERT}, author={Lee, Junbum}, booktitle={Proceedings of the 32nd Annual Conference on Human and Cognitive Language Technology}, pages={437--440}, year={2020} }
eunjin/koMHBERT-krbert-based-v1
aec63523a265c378c86c57ef463fae0437c60434
2021-06-05T17:45:36.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
eunjin
null
eunjin/koMHBERT-krbert-based-v1
4
null
transformers
18,582
korean Mental Health BERT huggingface에 공개된 KR-Medium BERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다. datasets from AIhub 웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
evandrodiniz/autonlp-api-boamente-417310788
e93cd663824d21bef56eeab69392632339ed6c97
2021-12-14T18:38:02.000Z
[ "pytorch", "bert", "text-classification", "unk", "dataset:evandrodiniz/autonlp-data-api-boamente", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
evandrodiniz
null
evandrodiniz/autonlp-api-boamente-417310788
4
null
transformers
18,583
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - evandrodiniz/autonlp-data-api-boamente co2_eq_emissions: 6.826886567147602 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 417310788 - CO2 Emissions (in grams): 6.826886567147602 ## Validation Metrics - Loss: 0.20949310064315796 - Accuracy: 0.9578392621870883 - Precision: 0.9476190476190476 - Recall: 0.9045454545454545 - AUC: 0.9714032720526227 - F1: 0.9255813953488372 ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/evandrodiniz/autonlp-api-boamente-417310788 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("evandrodiniz/autonlp-api-boamente-417310788", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("evandrodiniz/autonlp-api-boamente-417310788", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
evandrodiniz/autonlp-api-boamente-417310793
2e93ad9ab4b2fc417412399bf8c74fe9dc2d3b30
2021-12-14T18:39:10.000Z
[ "pytorch", "bert", "text-classification", "unk", "dataset:evandrodiniz/autonlp-data-api-boamente", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
evandrodiniz
null
evandrodiniz/autonlp-api-boamente-417310793
4
null
transformers
18,584
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - evandrodiniz/autonlp-data-api-boamente co2_eq_emissions: 9.446754273734577 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 417310793 - CO2 Emissions (in grams): 9.446754273734577 ## Validation Metrics - Loss: 0.25755178928375244 - Accuracy: 0.9407114624505929 - Precision: 0.8600823045267489 - Recall: 0.95 - AUC: 0.9732501264968797 - F1: 0.9028077753779697 ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/evandrodiniz/autonlp-api-boamente-417310793 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("evandrodiniz/autonlp-api-boamente-417310793", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
facebook/s2t-small-covost2-en-de-st
a61c96820ff12f2a916e2437c9342c8b8d321ed1
2022-02-07T15:15:09.000Z
[ "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "en", "de", "dataset:covost2", "arxiv:2010.05171", "arxiv:1912.06670", "arxiv:1904.08779", "transformers", "audio", "speech-translation", "license:mit" ]
automatic-speech-recognition
false
facebook
null
facebook/s2t-small-covost2-en-de-st
4
null
transformers
18,585
--- language: - en - de datasets: - covost2 tags: - audio - speech-translation - automatic-speech-recognition 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 --- # S2T-SMALL-COVOST2-EN-DE-ST `s2t-small-covost2-en-de-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). 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 a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to German text translation. 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.* 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 import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-en-de-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-en-de-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-en-de-st is trained on English-German subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## 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 character based SentencePiece vocab. ### 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. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for en-de (BLEU score): 16.29 ### 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}, } ```
facebook/s2t-small-covost2-en-et-st
c24d81b07cda06d0750fa31356becb3dd33bd32c
2022-02-07T15:31:40.000Z
[ "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "en", "et", "dataset:covost2", "arxiv:2010.05171", "arxiv:1912.06670", "arxiv:1904.08779", "transformers", "audio", "speech-translation", "license:mit" ]
automatic-speech-recognition
false
facebook
null
facebook/s2t-small-covost2-en-et-st
4
null
transformers
18,586
--- language: - en - et datasets: - covost2 tags: - audio - speech-translation - automatic-speech-recognition 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 --- # S2T-SMALL-COVOST2-EN-ET-ST `s2t-small-covost2-en-et-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). 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 a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Estonian text translation. 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.* 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 import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-en-et-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-en-et-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-en-et-st is trained on English-Estonian subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## 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 character based SentencePiece vocab. ### 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. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for en-et (BLEU score): 13.01 ### 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}, } ```
facebook/wav2vec2-base-10k-voxpopuli-ft-es
1f8c1fd6048a71c6c7644224c5a0aa87fb92cc27
2021-07-06T01:49:29.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "es", "arxiv:2101.00390", "transformers", "audio", "voxpopuli", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-10k-voxpopuli-ft-es
4
null
transformers
18,587
--- language: es tags: - audio - automatic-speech-recognition - voxpopuli license: cc-by-nc-4.0 --- # Wav2Vec2-Base-VoxPopuli-Finetuned [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in es (refer to Table 1 of paper for more information). **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI* See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/) # Usage for inference In the following it is shown how the model can be used in inference on a sample of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets) ```python #!/usr/bin/env python3 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torchaudio import torch # resample audio # load model & processor model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-es") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-es") # load dataset ds = load_dataset("common_voice", "es", split="validation[:1%]") # common voice does not match target sampling rate common_voice_sample_rate = 48000 target_sample_rate = 16000 resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate) # define mapping fn to read in sound file and resample def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) speech = resampler(speech) batch["speech"] = speech[0] return batch # load all audio files ds = ds.map(map_to_array) # run inference on the first 5 data samples inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True) # inference logits = model(**inputs).logits predicted_ids = torch.argmax(logits, axis=-1) print(processor.batch_decode(predicted_ids)) ```
fadhilarkan/distilbert-base-uncased-finetuned-cola-3
83c372f77b17051ee4f502140387c34a1a604a05
2021-11-12T18:12:25.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
fadhilarkan
null
fadhilarkan/distilbert-base-uncased-finetuned-cola-3
4
null
transformers
18,588
--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola-3 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-cola-3 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: 0.0002 - Matthews Correlation: 1.0 Label 0 : "AIMX" Label 1 : "OWNX" Label 2 : "CONT" Label 3 : "BASE" Label 4 : "MISC" ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 192 | 0.0060 | 1.0 | | No log | 2.0 | 384 | 0.0019 | 1.0 | | 0.0826 | 3.0 | 576 | 0.0010 | 1.0 | | 0.0826 | 4.0 | 768 | 0.0006 | 1.0 | | 0.0826 | 5.0 | 960 | 0.0005 | 1.0 | | 0.001 | 6.0 | 1152 | 0.0004 | 1.0 | | 0.001 | 7.0 | 1344 | 0.0003 | 1.0 | | 0.0005 | 8.0 | 1536 | 0.0003 | 1.0 | | 0.0005 | 9.0 | 1728 | 0.0002 | 1.0 | | 0.0005 | 10.0 | 1920 | 0.0002 | 1.0 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
federicopascual/distilbert-base-uncased-finetuned-cola
bb6c34815b83e521276e6e9861374cb24393462d
2021-12-24T21:52:47.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
federicopascual
null
federicopascual/distilbert-base-uncased-finetuned-cola
4
null
transformers
18,589
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5370037450559281 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7480 - Matthews Correlation: 0.5370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5292 | 1.0 | 535 | 0.5110 | 0.4239 | | 0.3508 | 2.0 | 1070 | 0.4897 | 0.4993 | | 0.2346 | 3.0 | 1605 | 0.6275 | 0.5029 | | 0.1806 | 4.0 | 2140 | 0.7480 | 0.5370 | | 0.1291 | 5.0 | 2675 | 0.8841 | 0.5200 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
fgaim/tiroberta-sentiment
d1cf1d2004813897d23ebd6c1c06d859dd8a0928
2022-05-14T06:47:23.000Z
[ "pytorch", "roberta", "text-classification", "ti", "dataset:TLMD", "transformers", "model-index" ]
text-classification
false
fgaim
null
fgaim/tiroberta-sentiment
4
1
transformers
18,590
--- language: ti widget: - text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር" datasets: - TLMD metrics: - accuracy - f1 - precision - recall model-index: - name: tiroberta-sentiment results: - task: name: Text Classification type: text-classification metrics: - name: Accuracy type: accuracy value: 0.828 - name: F1 type: f1 value: 0.8476527900797165 - name: Precision type: precision value: 0.760731319554849 - name: Recall type: recall value: 0.957 --- # Sentiment Analysis for Tigrinya with TiRoBERTa This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/roberta-base-tigrinya) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020). ## Basic usage ```python from transformers import pipeline ti_sent = pipeline("sentiment-analysis", model="fgaim/tiroberta-sentiment") ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር") ``` ## Training ### Hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Results It achieves the following results on the evaluation set: - F1: 0.8477 - Precision: 0.7607 - Recall: 0.957 - Accuracy: 0.828 - Loss: 0.6796 ### Framework versions - Transformers 4.10.3 - Pytorch 1.9.0+cu111 - Datasets 1.10.2 - Tokenizers 0.10.1 ## Citation If you use this model in your product or research, please cite as follows: ``` @article{Fitsum2021TiPLMs, author={Fitsum Gaim and Wonsuk Yang and Jong C. Park}, title={Monolingual Pre-trained Language Models for Tigrinya}, year=2021, publisher={WiNLP 2021/EMNLP 2021} } ``` ## References ``` Tela, A., Woubie, A. and Hautamäki, V. 2020. Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya. ArXiv, abs/2006.07698. ```
finiteautomata/bert-non-contextualized-hate-speech-es
d8930308341efd48d0f3c5af298549d49a3436e1
2021-05-19T16:52:40.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
finiteautomata
null
finiteautomata/bert-non-contextualized-hate-speech-es
4
null
transformers
18,591
Entry not found
flax-community/wav2vec2-base-persian
b2f4c8b02e6b71173ce1b72dc4841fb359e1bce3
2021-07-18T05:44:28.000Z
[ "pytorch", "jax", "tensorboard", "wav2vec2", "pretraining", "fa", "dataset:common_voice", "transformers", "speech", "license:apache-2.0" ]
null
false
flax-community
null
flax-community/wav2vec2-base-persian
4
1
transformers
18,592
--- language: fa datasets: - common_voice tags: - speech license: apache-2.0 --- # Wav2Vec2 4 Persian > This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-wav2vec2-in-persian/8180), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team Members - Mehrdad Farahani ([m3hrdadfi](https://huggingface.co/m3hrdadfi)) ## Dataset TODO: Update ## How To Use TODO: Update ## Demo TODO: Update ## Evaluation TODO: Update
flboehm/reddit-bert-text_5
c12f71bb32f95c144e96c5989402c80bce762161
2021-12-18T12:05:58.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
flboehm
null
flboehm/reddit-bert-text_5
4
null
transformers
18,593
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: reddit-bert-text5 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. --> # reddit-bert-text5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0257 | 1.0 | 945 | 2.6167 | | 2.7138 | 2.0 | 1890 | 2.5529 | | 2.6363 | 3.0 | 2835 | 2.5463 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
formermagic/codet5-base
7b85f801367ff55c09bcccdb4a899c93f1c693b7
2021-09-19T13:30:39.000Z
[ "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
formermagic
null
formermagic/codet5-base
4
1
transformers
18,594
Entry not found
fspanda/Electra-Medical-v1.5-discriminator
181be3758977eb479dad6d15c0ec7ccfa52cae2d
2020-11-04T15:00:32.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
fspanda
null
fspanda/Electra-Medical-v1.5-discriminator
4
null
transformers
18,595
Entry not found
g8a9/vit-geppetto-captioning
7f05a9fc08fdfc88d676fb17232fdd5a98b608ec
2021-11-29T09:57:21.000Z
[ "pytorch", "vision-encoder-decoder", "transformers" ]
null
false
g8a9
null
g8a9/vit-geppetto-captioning
4
null
transformers
18,596
Entry not found
gaotianyu1350/sup-simcse-bert-base-uncased
f938d5252193f7284296c621aff89b52ab7e7015
2021-05-19T17:03:12.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
gaotianyu1350
null
gaotianyu1350/sup-simcse-bert-base-uncased
4
null
transformers
18,597
Entry not found
gaotianyu1350/unsup-simcse-bert-base-uncased
e92bd5ed977474cc6743a41f5bad3d96227a1efe
2021-05-19T17:07:56.000Z
[ "pytorch", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
gaotianyu1350
null
gaotianyu1350/unsup-simcse-bert-base-uncased
4
null
transformers
18,598
Entry not found
garynguyen1174/disaster_tweet_bert
cc16b0b8664f36a0d5a1de8715640d84cc30e841
2021-06-06T01:05:17.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
garynguyen1174
null
garynguyen1174/disaster_tweet_bert
4
null
transformers
18,599
Entry not found