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599	abhishek/autonlp-imdb_eval-71421	[ "0", "1" ]	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-imdb_eval --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 71421 ## Validation Metrics - Loss: 0.4114699363708496 - Accuracy: 0.8248248248248248 - Precision: 0.8305439330543933 - Recall: 0.8085539714867617 - AUC: 0.9088033420466026 - F1: 0.8194014447884417 ## 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/abhishek/autonlp-imdb_eval-71421 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
600	abhishek/autonlp-imdb_sentiment_classification-31154	[ "0", "1" ]	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 31154 ## Validation Metrics - Loss: 0.19292379915714264 - Accuracy: 0.9395 - Precision: 0.9569557080474111 - Recall: 0.9204 - AUC: 0.9851040399999998 - F1: 0.9383219492302988 ## 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/abhishek/autonlp-imdb_sentiment_classification-31154 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb_sentiment_classification-31154", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb_sentiment_classification-31154", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
601	abhishek/autonlp-japanese-sentiment-59362	[ "negative", "positive" ]	--- tags: autonlp language: ja widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-japanese-sentiment --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 59362 ## Validation Metrics - Loss: 0.13092292845249176 - Accuracy: 0.9527127414314258 - Precision: 0.9634070704982427 - Recall: 0.9842171959602166 - AUC: 0.9667289746092403 - F1: 0.9737009564152002 ## 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/abhishek/autonlp-japanese-sentiment-59362 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
602	abhishek/autonlp-japanese-sentiment-59363	[ "negative", "positive" ]	--- tags: autonlp language: ja widget: - text: "🤗AutoNLPが大好きです" datasets: - abhishek/autonlp-data-japanese-sentiment --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 59363 ## Validation Metrics - Loss: 0.12651239335536957 - Accuracy: 0.9532079853817648 - Precision: 0.9729688278823665 - Recall: 0.9744633462616643 - AUC: 0.9717333684823413 - F1: 0.9737155136027014 ## 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/abhishek/autonlp-japanese-sentiment-59363 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-japanese-sentiment-59363", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-japanese-sentiment-59363", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
603	abhishek/autonlp-toxic-new-30516963	[ "False", "True" ]	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-toxic-new co2_eq_emissions: 30.684995819386277 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 30516963 - CO2 Emissions (in grams): 30.684995819386277 ## Validation Metrics - Loss: 0.08340361714363098 - Accuracy: 0.9688222161294113 - Precision: 0.9102096627164995 - Recall: 0.7692604006163328 - AUC: 0.9859340458715813 - F1: 0.8338204592901879 ## 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/abhishek/autonlp-toxic-new-30516963 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
604	adam-chell/tweet-sentiment-analyzer	[ "NEG", "NEU", "POS" ]	This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets. email : adam.chellaoui@epfl.ch
605	adamlin/filter	[ "LABEL_0" ]	--- language: - en tags: - generated_from_trainer datasets: - glue model_index: - name: filter results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb --- <!-- 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. --> # filter This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the GLUE STSB 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: 6 - eval_batch_size: 6 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.8.2 - Pytorch 1.8.1+cu111 - Datasets 1.9.0 - Tokenizers 0.10.3
606	addy88/perceiver_imdb	[ "neg", "pos" ]	### How to use Here is how to use this model in PyTorch: ```python from transformers import PerceiverTokenizer, PerceiverForMaskedLM tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb") model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb") text = "This is an incomplete sentence where some words are missing." # prepare input encoding = tokenizer(text, padding="max_length", return_tensors="pt") # mask " missing.". Note that the model performs much better if the masked span starts with a space. encoding.input_ids[0, 52:61] = tokenizer.mask_token_id inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device) # forward pass outputs = model(inputs=inputs, attention_mask=input_mask) logits = outputs.logits masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1) print(tokenizer.decode(masked_tokens_predictions)) >>> should print " missing." ```
607	addy88/programming-lang-identifier	[ "go", "java", "javascript", "php", "python", "ruby" ]	This model is funetune version of Codebert in roberta. On CodeSearchNet. ### Quick start: from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier") model = AutoModelForSequenceClassification.from_pretrained("addy88/programming-lang-identifier") input_ids = tokenizer.encode(CODE_TO_IDENTIFY) logits = model(input_ids)[0] language_idx = logits.argmax() # index for the resulting label ###
608	adelgasmi/autonlp-kpmg_nlp-18833547	[ "0", "1", "2", "3", "4" ]	--- tags: autonlp language: ar widget: - text: "I love AutoNLP 🤗" datasets: - adelgasmi/autonlp-data-kpmg_nlp co2_eq_emissions: 64.58945483765274 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 18833547 - CO2 Emissions (in grams): 64.58945483765274 ## Validation Metrics - Loss: 0.14247722923755646 - Accuracy: 0.9586074193404036 - Macro F1: 0.9468339778730883 - Micro F1: 0.9586074193404036 - Weighted F1: 0.9585551117678807 - Macro Precision: 0.9445436604001405 - Micro Precision: 0.9586074193404036 - Weighted Precision: 0.9591405429662925 - Macro Recall: 0.9499427161888565 - Micro Recall: 0.9586074193404036 - Weighted Recall: 0.9586074193404036 ## 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/adelgasmi/autonlp-kpmg_nlp-18833547 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
610	Jackett/subject_classifier	[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3" ]	Label association {'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
611	adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717	[ "Dance", "Heavy Metal", "Hip Hop", "Indie", "Pop", "Rock" ]	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - adrianmoses/autonlp-data-auto-nlp-lyrics-classification co2_eq_emissions: 88.89388195672073 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 19333717 - CO2 Emissions (in grams): 88.89388195672073 ## Validation Metrics - Loss: 1.0499154329299927 - Accuracy: 0.6207088513638894 - Macro F1: 0.46250803661544765 - Micro F1: 0.6207088513638894 - Weighted F1: 0.5850362079928957 - Macro Precision: 0.6451479987704787 - Micro Precision: 0.6207088513638894 - Weighted Precision: 0.6285080101186085 - Macro Recall: 0.4405680478429344 - Micro Recall: 0.6207088513638894 - Weighted Recall: 0.6207088513638894 ## 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/adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
613	ahmedrachid/FinancialBERT-Sentiment-Analysis	[ "negative", "neutral", "positive" ]	--- language: en tags: - financial-sentiment-analysis - sentiment-analysis datasets: - financial_phrasebank widget: - text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales. - text: Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000. - text: Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008. --- ### FinancialBERT for Sentiment Analysis [FinancialBERT](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining ### Training data FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). ### Fine-tuning hyper-parameters - learning_rate = 2e-5 - batch_size = 32 - max_seq_length = 512 - num_train_epochs = 5 ### Evaluation metrics The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. \| sentiment \| precision \| recall \| f1-score \| support \| \| ------------- \|:-------------:\|:-------------:\|:-------------:\| -----:\| \| negative \| 0.96 \| 0.97 \| 0.97 \| 58 \| \| neutral \| 0.98 \| 0.99 \| 0.98 \| 279 \| \| positive \| 0.98 \| 0.97 \| 0.97 \| 148 \| \| macro avg \| 0.97 \| 0.98 \| 0.98 \| 485 \| \| weighted avg \| 0.98 \| 0.98 \| 0.98 \| 485 \| ### How to use The model can be used thanks to Transformers pipeline for sentiment analysis. ```python from transformers import BertTokenizer, BertForSequenceClassification from transformers import pipeline model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3) tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.", "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", ] results = nlp(sentences) print(results) [{'label': 'positive', 'score': 0.9998133778572083}, {'label': 'neutral', 'score': 0.9997822642326355}, {'label': 'negative', 'score': 0.9877365231513977}] ``` > Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)
614	ainize/klue-bert-base-re	[ "LABEL_0", "LABEL_1", "LABEL_10", "LABEL_11", "LABEL_12", "LABEL_13", "LABEL_14", "LABEL_15", "LABEL_16", "LABEL_17", "LABEL_18", "LABEL_19", "LABEL_2", "LABEL_20", "LABEL_21", "LABEL_22", "LABEL_23", "LABEL_24", "LABEL_25", "LABEL_26", "LABEL_27", "LABEL_28", "LABEL_29", "LABEL_3", "LABEL_4", "LABEL_5", "LABEL_6", "LABEL_7", "LABEL_8", "LABEL_9" ]	# bert-base for KLUE Relation Extraction task. Fine-tuned klue/bert-base using KLUE RE dataset. - <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a> - <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a> - <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Github</a> - Run KLUE RE on free GPU : <a href="https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ainize-team/klue-re-workspace">Ainize Workspace</a> <br> # Usage <pre><code> from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-re") model = AutoModelForSequenceClassification.from_pretrained("ainize/klue-bert-base-re") # Add "&ltsubj&gt", "&lt/subj&gt" to both ends of the subject object and "&ltobj&gt", "&lt/obj&gt" to both ends of the object object. sentence = "&ltsubj&gt손흥민&lt/subj&gt은 &ltobj&gt대한민국&lt/obj&gt에서 태어났다." encodings = tokenizer(sentence, max_length=128, truncation=True, padding="max_length", return_tensors="pt") outputs = model(**encodings) logits = outputs['logits'] preds = torch.argmax(logits, dim=1) </code></pre> <br> # About us - <a href="https://ainize.ai/teachable-nlp">Teachable NLP</a> - Train NLP models with your own text without writing any code - <a href="https://ainize.ai/">Ainize</a> - Deploy ML project using free gpu
617	akahana/indonesia-emotion-roberta	[ "SEDIH", "MARAH", "CINTA", "TAKUT", "BAHAGIA" ]	--- language: "id" widget: - text: "dia orang yang baik ya bunds." --- ## how to use ```python from transformers import pipeline, set_seed path = "akahana/indonesia-emotion-roberta" emotion = pipeline('text-classification', model=path,device=0) set_seed(42) kalimat = "dia orang yang baik ya bunds." preds = emotion(kalimat) preds [{'label': 'BAHAGIA', 'score': 0.8790940046310425}] ```
618	akahana/indonesia-sentiment-roberta	[ "POSITIF", "NETRAL", "NEGATIF" ]	--- language: "id" widget: - text: "dia orang yang baik ya bunds." --- ## how to use ```python from transformers import pipeline, set_seed path = "akahana/indonesia-sentiment-roberta" emotion = pipeline('text-classification', model=path,device=0) set_seed(42) kalimat = "dia orang yang baik ya bunds." preds = emotion(kalimat) preds ```
619	akdeniz27/bert-turkish-text-classification	[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5", "LABEL_6", "LABEL_7", "LABEL_8" ]	--- language: tr --- # Turkish Text Classification for Complaints Data Set This model is a fine-tune model of https://github.com/stefan-it/turkish-bert by using text classification data with 9 categories as follows: id_to_category = {0: 'KONFORSUZLUK', 1: 'TARİFE İHLALİ', 2: 'DURAKTA DURMAMA', 3: 'ŞOFÖR-PERSONEL ŞİKAYETİ', 4: 'YENİ GÜZERGAH/HAT/DURAK İSTEĞİ', 5: 'TRAFİK GÜVENLİĞİ', 6: 'DİĞER ŞİKAYETLER', 7: 'TEŞEKKÜR', 8: 'DİĞER TALEPLER'}
620	akhooli/xlm-r-large-arabic-sent	[ "LABEL_0_mixed", "LABEL_1_neg", "LABEL_2_pos" ]	--- language: - ar - en - multilingual license: mit --- ### xlm-r-large-arabic-sent Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large. Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may confuse other classes (was based on a rate of 3 out of 5 in reviews). Usage: see last section in this [Colab notebook](https://lnkd.in/d3bCFyZ)
621	akhooli/xlm-r-large-arabic-toxic	[ "LABEL_0_negative", "LABEL_1_positive" ]	--- language: - ar - en license: mit --- ### xlm-r-large-arabic-toxic (toxic/hate speech classifier) Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large. Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Usage and further info: see last section in this [Colab notebook](https://lnkd.in/d3bCFyZ)
622	akilesh96/autonlp-mrcooper_text_classification-529614927	[ "Animals", "Compliment", "Education", "Health", "Heavy Emotion", "Joke", "Love", "Politics", "Religion", "Science", "Self" ]	--- tags: autonlp language: en widget: - text: "Not Many People Know About The City 1200 Feet Below Detroit" - text: "Bob accepts the challenge, and the next week they're standing in Saint Peters square. 'This isnt gonna work, he's never going to see me here when theres this much people. You stay here, I'll go talk to him and you'll see me on the balcony, the guards know me too.' Half an hour later, Bob and the pope appear side by side on the balcony. Bobs boss gets a heart attack, and Bob goes to visit him in the hospital." - text: "I’m sorry if you made it this far, but I’m just genuinely idk, I feel like I shouldn’t give up, it’s just getting harder to come back from stuff like this." datasets: - akilesh96/autonlp-data-mrcooper_text_classification co2_eq_emissions: 5.999771405025692 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 529614927 - CO2 Emissions (in grams): 5.999771405025692 ## Validation Metrics - Loss: 0.7582379579544067 - Accuracy: 0.7636103151862464 - Macro F1: 0.770630619486531 - Micro F1: 0.7636103151862464 - Weighted F1: 0.765233270165301 - Macro Precision: 0.7746285216467107 - Micro Precision: 0.7636103151862464 - Weighted Precision: 0.7683270753840836 - Macro Recall: 0.7680576576961138 - Micro Recall: 0.7636103151862464 - Weighted Recall: 0.7636103151862464 ## 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/akilesh96/autonlp-mrcooper_text_classification-529614927 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
623	akshara23/distilbert-base-uncased-finetuned-cola	[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model_index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification metric: name: Matthews Correlation type: matthews_correlation value: 0.6290322580645161 --- <!-- 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 an unkown dataset. It achieves the following results on the evaluation set: - Loss: 1.0475 - Matthews Correlation: 0.6290 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 16 \| 1.3863 \| 0.0 \| \| No log \| 2.0 \| 32 \| 1.2695 \| 0.4503 \| \| No log \| 3.0 \| 48 \| 1.1563 \| 0.6110 \| \| No log \| 4.0 \| 64 \| 1.0757 \| 0.6290 \| \| No log \| 5.0 \| 80 \| 1.0475 \| 0.6290 \| ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
624	albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135	[ "0", "1", "2", "3", "4", "5" ]	--- tags: autonlp language: bn widget: - text: "I love AutoNLP 🤗" datasets: - albertvillanova/autonlp-data-indic_glue-multi_class_classification-1e67664 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 1311135 ## Validation Metrics - Loss: 0.35616958141326904 - Accuracy: 0.8979447200566973 - Macro F1: 0.8545383956197669 - Micro F1: 0.8979447200566975 - Weighted F1: 0.8983951947775538 - Macro Precision: 0.8615833774439791 - Micro Precision: 0.8979447200566973 - Weighted Precision: 0.9013559365881655 - Macro Recall: 0.8516503001777104 - Micro Recall: 0.8979447200566973 - Weighted Recall: 0.8979447200566973 ## 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/albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("albertvillanova/autonlp-indic_glue-multi_class_classification-1e67664-1311135", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
625	alecmullen/autonlp-group-classification-441411446	[ "Beauty", "Business/Finance", "Faith", "Fitness", "Food", "Gaming", "Local", "Marketplace", "Memes", "Music", "None", "Social", "Sports", "TV/Movies", "Travel" ]	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - alecmullen/autonlp-data-group-classification co2_eq_emissions: 0.4362732160754736 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 441411446 - CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics - Loss: 0.7598486542701721 - Accuracy: 0.8222222222222222 - Macro F1: 0.2912091747693842 - Micro F1: 0.8222222222222222 - Weighted F1: 0.7707160863181806 - Macro Precision: 0.29631463146314635 - Micro Precision: 0.8222222222222222 - Weighted Precision: 0.7341339689524508 - Macro Recall: 0.30174603174603176 - Micro Recall: 0.8222222222222222 - Weighted Recall: 0.8222222222222222 ## 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/alecmullen/autonlp-group-classification-441411446 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
627	ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 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. --> # finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7600 - Accuracy: 0.8144 - F1: 0.8788 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3514 \| 0.8427 \| 0.8979 \| \| No log \| 2.0 \| 390 \| 0.3853 \| 0.8293 \| 0.8936 \| \| 0.3147 \| 3.0 \| 585 \| 0.5494 \| 0.8268 \| 0.8868 \| \| 0.3147 \| 4.0 \| 780 \| 0.6235 \| 0.8427 \| 0.8995 \| \| 0.3147 \| 5.0 \| 975 \| 0.8302 \| 0.8378 \| 0.8965 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
628	ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 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. --> # finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4064 - Accuracy: 0.8289 - F1: 0.8901 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4163 \| 0.8085 \| 0.8780 \| \| No log \| 2.0 \| 390 \| 0.4098 \| 0.8268 \| 0.8878 \| \| 0.312 \| 3.0 \| 585 \| 0.5892 \| 0.8244 \| 0.8861 \| \| 0.312 \| 4.0 \| 780 \| 0.7580 \| 0.8232 \| 0.8845 \| \| 0.312 \| 5.0 \| 975 \| 0.9028 \| 0.8183 \| 0.8824 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
629	ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 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. --> # finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3825 - Accuracy: 0.8144 - F1: 0.8833 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3975 \| 0.8122 \| 0.8795 \| \| No log \| 2.0 \| 390 \| 0.4376 \| 0.8085 \| 0.8673 \| \| 0.3169 \| 3.0 \| 585 \| 0.5736 \| 0.8171 \| 0.8790 \| \| 0.3169 \| 4.0 \| 780 \| 0.8178 \| 0.8098 \| 0.8754 \| \| 0.3169 \| 5.0 \| 975 \| 0.9244 \| 0.8073 \| 0.8738 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
630	ali2066/finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 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. --> # finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.9772 - F1: 0.9883 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 104 \| 0.0539 \| 0.9885 \| 0.9942 \| \| No log \| 2.0 \| 208 \| 0.0282 \| 0.9885 \| 0.9942 \| \| No log \| 3.0 \| 312 \| 0.0317 \| 0.9914 \| 0.9956 \| \| No log \| 4.0 \| 416 \| 0.0462 \| 0.9885 \| 0.9942 \| \| 0.0409 \| 5.0 \| 520 \| 0.0517 \| 0.9885 \| 0.9942 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
631	ali2066/finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 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. --> # finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3358 - Accuracy: 0.8688 - F1: 0.9225 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 81 \| 0.4116 \| 0.8382 \| 0.9027 \| \| No log \| 2.0 \| 162 \| 0.4360 \| 0.8382 \| 0.8952 \| \| No log \| 3.0 \| 243 \| 0.5719 \| 0.8382 \| 0.8995 \| \| No log \| 4.0 \| 324 \| 0.7251 \| 0.8493 \| 0.9021 \| \| No log \| 5.0 \| 405 \| 0.8384 \| 0.8456 \| 0.9019 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
632	ali2066/finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 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. --> # finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5777 - Accuracy: 0.6794 - F1: 0.5010 ## 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: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 48 \| 0.6059 \| 0.63 \| 0.4932 \| \| No log \| 2.0 \| 96 \| 0.6327 \| 0.705 \| 0.5630 \| \| No log \| 3.0 \| 144 \| 0.7003 \| 0.695 \| 0.5197 \| \| No log \| 4.0 \| 192 \| 0.9368 \| 0.69 \| 0.4655 \| \| No log \| 5.0 \| 240 \| 1.1935 \| 0.685 \| 0.4425 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
633	ali2066/finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 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. --> # finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy: 0.8138 - F1: 0.8785 - Precision: 0.8489 - Recall: 0.9101 ## 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: 1e-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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 390 \| 0.4335 \| 0.7732 \| 0.8533 \| 0.8209 \| 0.8883 \| \| 0.5141 \| 2.0 \| 780 \| 0.4196 \| 0.8037 \| 0.8721 \| 0.8446 \| 0.9015 \| \| 0.3368 \| 3.0 \| 1170 \| 0.4519 \| 0.8098 \| 0.8779 \| 0.8386 \| 0.9212 \| \| 0.2677 \| 4.0 \| 1560 \| 0.4787 \| 0.8122 \| 0.8785 \| 0.8452 \| 0.9146 \| \| 0.2677 \| 5.0 \| 1950 \| 0.4912 \| 0.8146 \| 0.8794 \| 0.8510 \| 0.9097 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
634	ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51	[ "NEGATIVE", "POSITIVE" ]	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 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. --> # finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4563 - Accuracy: 0.8440 - F1: 0.8954 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4302 \| 0.8073 \| 0.8754 \| \| No log \| 2.0 \| 390 \| 0.3970 \| 0.8220 \| 0.8875 \| \| 0.3703 \| 3.0 \| 585 \| 0.3972 \| 0.8402 \| 0.8934 \| \| 0.3703 \| 4.0 \| 780 \| 0.4945 \| 0.8390 \| 0.8935 \| \| 0.3703 \| 5.0 \| 975 \| 0.5354 \| 0.8305 \| 0.8898 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
635	ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 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. --> # finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4208 - Accuracy: 0.8283 - F1: 0.8915 - Precision: 0.8487 - Recall: 0.9389 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 390 \| 0.4443 \| 0.7768 \| 0.8589 \| 0.8072 \| 0.9176 \| \| 0.4532 \| 2.0 \| 780 \| 0.4603 \| 0.8098 \| 0.8791 \| 0.8302 \| 0.9341 \| \| 0.2608 \| 3.0 \| 1170 \| 0.5284 \| 0.8061 \| 0.8713 \| 0.8567 \| 0.8863 \| \| 0.1577 \| 4.0 \| 1560 \| 0.6398 \| 0.8085 \| 0.8749 \| 0.8472 \| 0.9044 \| \| 0.1577 \| 5.0 \| 1950 \| 0.7089 \| 0.8085 \| 0.8741 \| 0.8516 \| 0.8979 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
636	ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 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. --> # finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.8286 - F1: 0.8887 - Precision: 0.8628 - Recall: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 390 \| 0.3890 \| 0.8110 \| 0.8749 \| 0.8631 \| 0.8871 \| \| 0.4535 \| 2.0 \| 780 \| 0.3921 \| 0.8439 \| 0.8984 \| 0.8721 \| 0.9264 \| \| 0.266 \| 3.0 \| 1170 \| 0.4454 \| 0.8415 \| 0.8947 \| 0.8860 \| 0.9034 \| \| 0.16 \| 4.0 \| 1560 \| 0.5610 \| 0.8427 \| 0.8957 \| 0.8850 \| 0.9067 \| \| 0.16 \| 5.0 \| 1950 \| 0.6180 \| 0.8488 \| 0.9010 \| 0.8799 \| 0.9231 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
637	ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 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. --> # finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4345 - Accuracy: 0.8321 - F1: 0.8904 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3922 \| 0.8061 \| 0.8747 \| \| No log \| 2.0 \| 390 \| 0.3764 \| 0.8171 \| 0.8837 \| \| 0.4074 \| 3.0 \| 585 \| 0.3873 \| 0.8220 \| 0.8843 \| \| 0.4074 \| 4.0 \| 780 \| 0.4361 \| 0.8232 \| 0.8854 \| \| 0.4074 \| 5.0 \| 975 \| 0.4555 \| 0.8159 \| 0.8793 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
638	ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47 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. --> # finetuned_sentence_itr0_2e-05_all_27_02_2022-17_27_47 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5002 - Accuracy: 0.8103 - F1: 0.8764 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4178 \| 0.7963 \| 0.8630 \| \| No log \| 2.0 \| 390 \| 0.3935 \| 0.8061 \| 0.8770 \| \| 0.4116 \| 3.0 \| 585 \| 0.4037 \| 0.8085 \| 0.8735 \| \| 0.4116 \| 4.0 \| 780 \| 0.4696 \| 0.8146 \| 0.8796 \| \| 0.4116 \| 5.0 \| 975 \| 0.4849 \| 0.8207 \| 0.8823 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
639	ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 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. --> # finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8231 - F1: 0.8833 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3883 \| 0.8146 \| 0.8833 \| \| No log \| 2.0 \| 390 \| 0.3607 \| 0.8390 \| 0.8964 \| \| 0.4085 \| 3.0 \| 585 \| 0.3812 \| 0.8488 \| 0.9042 \| \| 0.4085 \| 4.0 \| 780 \| 0.3977 \| 0.8549 \| 0.9077 \| \| 0.4085 \| 5.0 \| 975 \| 0.4233 \| 0.8573 \| 0.9092 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
640	ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 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. --> # finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4638 - Accuracy: 0.8247 - F1: 0.8867 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4069 \| 0.7976 \| 0.875 \| \| No log \| 2.0 \| 390 \| 0.4061 \| 0.8134 \| 0.8838 \| \| 0.4074 \| 3.0 \| 585 \| 0.4075 \| 0.8134 \| 0.8798 \| \| 0.4074 \| 4.0 \| 780 \| 0.4746 \| 0.8256 \| 0.8885 \| \| 0.4074 \| 5.0 \| 975 \| 0.4881 \| 0.8220 \| 0.8845 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
641	ali2066/finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 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. --> # finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0914 - Accuracy: 0.9746 - F1: 0.9870 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 104 \| 0.0501 \| 0.9828 \| 0.9913 \| \| No log \| 2.0 \| 208 \| 0.0435 \| 0.9828 \| 0.9913 \| \| No log \| 3.0 \| 312 \| 0.0414 \| 0.9828 \| 0.9913 \| \| No log \| 4.0 \| 416 \| 0.0424 \| 0.9799 \| 0.9898 \| \| 0.0547 \| 5.0 \| 520 \| 0.0482 \| 0.9828 \| 0.9913 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
642	ali2066/finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 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. --> # finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3455 - Accuracy: 0.8609 - F1: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 81 \| 0.4468 \| 0.8235 \| 0.8929 \| \| No log \| 2.0 \| 162 \| 0.4497 \| 0.8382 \| 0.9 \| \| No log \| 3.0 \| 243 \| 0.4861 \| 0.8309 \| 0.8940 \| \| No log \| 4.0 \| 324 \| 0.5087 \| 0.8235 \| 0.8879 \| \| No log \| 5.0 \| 405 \| 0.5228 \| 0.8199 \| 0.8858 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
643	ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55	[ "NEGATIVE", "POSITIVE" ]	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 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. --> # finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7224 - Accuracy: 0.6979 - F1: 0.4736 - Precision: 0.5074 - Recall: 0.4440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 95 \| 0.6009 \| 0.65 \| 0.2222 \| 0.625 \| 0.1351 \| \| No log \| 2.0 \| 190 \| 0.6140 \| 0.675 \| 0.3689 \| 0.6552 \| 0.2568 \| \| No log \| 3.0 \| 285 \| 0.6580 \| 0.67 \| 0.4590 \| 0.5833 \| 0.3784 \| \| No log \| 4.0 \| 380 \| 0.7560 \| 0.665 \| 0.4806 \| 0.5636 \| 0.4189 \| \| No log \| 5.0 \| 475 \| 0.8226 \| 0.665 \| 0.464 \| 0.5686 \| 0.3919 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3