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kleinay/qasrl-seq2seq-model | d4ab640472add4bbcc611d7a2e832752591e8ed6 | 2022-06-03T08:08:43.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kleinay | null | kleinay/qasrl-seq2seq-model | 1 | null | transformers | 32,600 | Entry not found |
erickfm/t5-large-finetuned-bias-v6 | f400c4876c20fb2a5ad9937705e70fba941f67d4 | 2022-06-04T08:56:19.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-large-finetuned-bias-v6 | 1 | null | transformers | 32,601 | Entry not found |
Bistolero/en_ge_20_20 | 58b83b85a25fa8ef43bb80a6423338854b4df8ce | 2022-06-03T10:26:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Bistolero | null | Bistolero/en_ge_20_20 | 1 | null | transformers | 32,602 | Entry not found |
PontifexMaximus/mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en | d6755563f63d51b972a45594dd6579de77bc24c1 | 2022-06-07T15:17:41.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:opus_infopankki",
"transformers",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en | 1 | null | transformers | 32,603 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- opus_infopankki
metrics:
- bleu
model-index:
- name: mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_infopankki
type: opus_infopankki
args: en-fa
metrics:
- name: Bleu
type: bleu
value: 15.1329
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
This model is a fine-tuned version of [persiannlp/mt5-small-parsinlu-opus-translation_fa_en](https://huggingface.co/persiannlp/mt5-small-parsinlu-opus-translation_fa_en) on the opus_infopankki dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9193
- Bleu: 15.1329
- Gen Len: 13.4603
## 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-06
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 3.1182 | 1.0 | 1807 | 2.5985 | 10.6445 | 13.7938 |
| 2.8377 | 2.0 | 3614 | 2.3799 | 11.852 | 13.6168 |
| 2.6644 | 3.0 | 5421 | 2.2426 | 12.877 | 13.5768 |
| 2.5286 | 4.0 | 7228 | 2.1521 | 13.5342 | 13.5567 |
| 2.4523 | 5.0 | 9035 | 2.0801 | 14.0355 | 13.5387 |
| 2.4026 | 6.0 | 10842 | 2.0197 | 14.4284 | 13.4956 |
| 2.317 | 7.0 | 12649 | 1.9691 | 14.7776 | 13.4325 |
| 2.3174 | 8.0 | 14456 | 1.9373 | 15.189 | 13.4261 |
| 2.3374 | 9.0 | 16263 | 1.9393 | 15.1149 | 13.4087 |
| 2.3131 | 10.0 | 18070 | 1.9304 | 15.0654 | 13.4234 |
| 2.295 | 11.0 | 19877 | 1.9239 | 15.102 | 13.4443 |
| 2.3017 | 12.0 | 21684 | 1.9203 | 15.1676 | 13.4575 |
| 2.3153 | 13.0 | 23491 | 1.9193 | 15.1329 | 13.4603 |
| 2.2939 | 14.0 | 25298 | 1.9193 | 15.1329 | 13.4603 |
| 2.3241 | 15.0 | 27105 | 1.9193 | 15.1329 | 13.4603 |
| 2.3376 | 16.0 | 28912 | 1.9193 | 15.1329 | 13.4603 |
| 2.2859 | 17.0 | 30719 | 1.9193 | 15.1329 | 13.4603 |
| 2.3016 | 18.0 | 32526 | 1.9193 | 15.1329 | 13.4603 |
| 2.3101 | 19.0 | 34333 | 1.9193 | 15.1329 | 13.4603 |
| 2.3088 | 20.0 | 36140 | 1.9193 | 15.1329 | 13.4603 |
| 2.2833 | 21.0 | 37947 | 1.9193 | 15.1329 | 13.4603 |
| 2.2986 | 22.0 | 39754 | 1.9193 | 15.1329 | 13.4603 |
| 2.3254 | 23.0 | 41561 | 1.9193 | 15.1329 | 13.4603 |
| 2.3165 | 24.0 | 43368 | 1.9193 | 15.1329 | 13.4603 |
| 2.289 | 25.0 | 45175 | 1.9193 | 15.1329 | 13.4603 |
| 2.3212 | 26.0 | 46982 | 1.9193 | 15.1329 | 13.4603 |
| 2.2902 | 27.0 | 48789 | 1.9193 | 15.1329 | 13.4603 |
| 2.3026 | 28.0 | 50596 | 1.9193 | 15.1329 | 13.4603 |
| 2.2949 | 29.0 | 52403 | 1.9193 | 15.1329 | 13.4603 |
| 2.3152 | 30.0 | 54210 | 1.9193 | 15.1329 | 13.4603 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.7.1+cu110
- Datasets 2.2.2
- Tokenizers 0.12.1
|
arrandi/xlm-roberta-base-finetuned-panx-de | 42a34e7f4362918b675e5701e28f59cb8a20931d | 2022-06-03T14:27:43.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | arrandi | null | arrandi/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 32,604 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8620945214069894
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1372
- F1: 0.8621
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tdobrxl/opus-mt-en-vi-finetuned-IWSLT15 | 959db39d99018650b78b807a733991ceeed6aa97 | 2022-06-06T11:03:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tdobrxl | null | tdobrxl/opus-mt-en-vi-finetuned-IWSLT15 | 1 | null | transformers | 32,605 | # Overview
This is a fine-tuned version of the model [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi?text=My+name+is+Sarah+and+I+live+in+London) on the dataset [IWSLT'15 English-Vietnamese](https://huggingface.co/datasets/mt_eng_vietnamese).
Performance in terms of [sacrebleu](https://huggingface.co/docs/datasets/v1.5.0/using_metrics.html) on the test set is as follows:
* Original opus-mt-en-vi: 29.83
* Fine-tuned opus-mt-en-vi: 37.35
# Parameters
* learning_rate=2e-5
* batch_size: 32
* weight_decay=0.01
* num_train_epochs=1
# Thoughts
* Model `Helsinki-NLP/opus-mt-en-vi` is small (around 260MB), and can be easily deployed to a cheap server (e.g., EC2 t2.medium) without a GPU
* Easier and much faster to train compared to t5 or byt5. |
jppaolim/v48_GPT2Medium_PT | 44ea5e187f0cc74eafaaa206816b5e7bb3466747 | 2022-06-03T15:07:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jppaolim | null | jppaolim/v48_GPT2Medium_PT | 1 | null | transformers | 32,606 | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur wants to go to the beach. He calls the beach and asks for a spot on the sand. Arthur gets a new friend with a beach towel. Arthur takes the beach. Arthur spends the day relaxing and having a great time.
Arthur goes to the beach. Arthur is feeling really bored. He decides to go to the beach. He packs up his bags and drives to the beach. Arthur spends the next two days swimming at the beach. He is so glad he got to spend some time at the beach.
Arthur goes to the beach. Arthur really wanted to go to the beach. He went to the beach in his car. He spent the whole day on the beach. He finally had a great day of swimming. Arthur really enjoyed the beach.
Arthur goes to the beach. Arthur had always wanted to go to the beach. Arthur saved up his money for a few weeks. Arthur went to the beach with his savings. Arthur had a great time at the beach. Arthur is now planning his next trip.
Arthur goes to the beach. Arthur loves to go to the beach. He loves to go to the sand. Arthur took his friend with him to the beach. Arthur played in the ocean for a long time. Arthur got his sand and went home.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur is excited to go to the beach. Arthur packs his beach towel. Arthur drives to the beach. Arthur spends the entire day at the beach. Arthur has a great day at the beach.
Arthur goes to the beach. Arthur was a lonely boy who wanted a place to stay. His parents wanted him to go to the beach. They convinced Arthur to go to the beach with them. Arthur was so happy to go to the beach. He was so happy to get to play in the ocean with his friends.
Arthur goes to the beach. Arthur decided he needed to go to the beach. He called his friends to come and see the beach. They met up at the beach. Arthur and his friends went to the beach and played. Arthur went home and had a good day.
Arthur goes to the beach. Arthur is sitting at home reading a book. He decides he will play a game of basketball. Arthur decides to play a game of basketball. He plays his game with his family and friends. Arthur is very happy that he played basketball.
Arthur goes to the beach. Arthur and his friends went to the beach. Arthur found out that he had a bad sunburn. Arthur had to go to the doctor for his sunburn. The doctor recommended an ointment to Arthur. Arthur had no more bad sunburns after that.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is bored one afternoon. He calls his friend Frank and tells him to go to the beach. Arthur drives to the beach and spends the day playing in the sand. After the sun went down, Arthur went home and watched a movie. Now, Arthur is very tired after a long day of work.
Arthur goes to the beach. Arthur is bored one summer day. He asks his parents for some time off. His parents tell him he has to go the beach. Arthur packs up his car and takes a trip. When he gets back home, Arthur is happy that he went the beach.
Arthur goes to the beach. Arthur had always wanted to go to the beach. Arthur's friends encourage him to go. Finally Arthur agrees to go to the beach. At the beach he spends a very relaxing day at the beach. Arthur is glad that he went to the beach.
Arthur goes to the beach. Arthur wants to go to the beach. He gets his stuff together and drives to the beach. While on the beach he meets a nice young man named Dave. Dave and Arthur fall in love. Arthur and Dave become friends and start dating.
Arthur goes to the beach. Arthur is bored on a weekend afternoon. He decides to go to the beach. Arthur packs his beach bag and leaves. Arthur arrives at the beach. Arthur spends the day at the beach.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur has always wanted to go to the beach. He decides to go to the beach one day. At the beach he spends the entire day at the beach. He has a great time. Arthur is very happy that he went to the beach.
Arthur goes to the beach. Arthur is bored one day. He decides to go to the beach. When he gets there, he spends all day at the beach. Arthur has a great time at the beach. He will not leave the beach for another day.
Arthur goes to the beach. Arthur is bored on a summer day. He decides he wants to go to the beach. Arthur packs his bags and drives out to the ocean. Once at the beach, Arthur spends all day playing in the sand. Now that he has had so much fun, he plans to do it again soon.
Arthur goes to the beach. Arthur was feeling bored one day. He decided to go to the beach. Arthur went to the beach and played in the sand. Arthur felt so much better after playing in the sand. Arthur was glad he had gone to the beach.
Arthur goes to the beach. Arthur was excited for a day at the beach. He had packed his beach bag and his sunscreen. Arthur went to the beach with his friends. Arthur played in the sand all day. Arthur returned home with lots of sunburns.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is bored one day. He decides he would like a day off. Arthur calls his friends and tells them about the day. They all go to the beach and play games. Arthur has a great day at the beach.
Arthur goes to the beach. Arthur is bored one day. He decides he needs a way to pass the time. Arthur takes his beach bag and drives to the beach. Arthur spends the entire day at the beach. Arthur has a great day at the beach.
Arthur goes to the beach. Arthur is bored one day. He decides he would like a day off. So Arthur takes a trip to the beach. Arthur spends the entire day at the beach. Arthur has a great day at the beach.
Arthur goes to the beach. Arthur is bored one day. He decides to go to the beach. Arthur spends the entire day at the beach. When he gets home, Arthur feels happy. Now that he has gone to the beach, Arthur is no longer bored.
Arthur goes to the beach. Arthur is feeling bored one day. He decides he would like to go to the beach. Arthur packs up his beach bag and drives down to the beach. While at the beach, Arthur sees many people playing in the water. Arthur has a great time at the beach with his friends.
|
roshnir/xlmr-base-ft-mlqa-dev-en-hi | 36103165676c1cc6e265207325fe9c89f7bc466a | 2022-06-03T15:42:02.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-base-ft-mlqa-dev-en-hi | 1 | null | transformers | 32,607 | Entry not found |
Splend1dchan/xtreme_s_xlsr_t5lephone-small_residual_minds14.en-all | 2941f9c7f685a453b5d567ce3e7c859fad6d4b29 | 2022-06-04T05:15:02.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_xlsr_t5lephone-small_residual_minds14.en-all | 1 | null | transformers | 32,608 | Entry not found |
sayanmandal/t5-small_6_3-hinglish | 74bfad6171723766d3604ac2140631302e8996e9 | 2022-06-04T02:31:25.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sayanmandal | null | sayanmandal/t5-small_6_3-hinglish | 1 | null | transformers | 32,609 | Entry not found |
roshnir/mBert-finetuned-mlqa-dev-vi-hi | 6d16746ab215e23861d3230fc977ce1401e1aada | 2022-06-03T19:48:02.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/mBert-finetuned-mlqa-dev-vi-hi | 1 | null | transformers | 32,610 | Entry not found |
santiviquez/mt5-small-finetuned-samsum-en | 481c2e3f6d61aae6f8f5ee235040ba18f85a52fd | 2022-06-07T14:59:42.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | santiviquez | null | santiviquez/mt5-small-finetuned-samsum-en | 1 | null | transformers | 32,611 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-samsum-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-samsum-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4304
- Rouge1: 21.9966
- Rouge2: 9.1451
- Rougel: 19.532
- Rougelsum: 20.6359
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log | 1.0 | 125 | 4.0396 | 8.9392 | 1.5339 | 8.1146 | 8.538 |
| No log | 2.0 | 250 | 3.0166 | 17.0822 | 6.0564 | 15.1854 | 16.2353 |
| No log | 3.0 | 375 | 2.7375 | 18.9169 | 7.0912 | 16.8087 | 17.7473 |
| No log | 4.0 | 500 | 2.5996 | 20.5929 | 7.8755 | 18.2074 | 19.3914 |
| No log | 5.0 | 625 | 2.5095 | 21.1958 | 8.7027 | 18.8919 | 19.9921 |
| No log | 6.0 | 750 | 2.4641 | 21.2479 | 8.8452 | 18.9289 | 19.9557 |
| No log | 7.0 | 875 | 2.4341 | 22.1418 | 9.1294 | 19.6073 | 20.7666 |
| No log | 8.0 | 1000 | 2.4304 | 21.9966 | 9.1451 | 19.532 | 20.6359 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jgriffi/xlm-roberta-base-finetuned-panx-de-fr | 8bbdf7857a80b2c2fcc242ba11b93ad35de1cfd5 | 2022-06-03T23:42:57.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jgriffi | null | jgriffi/xlm-roberta-base-finetuned-panx-de-fr | 1 | null | transformers | 32,612 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1774
- F1: 0.8594
## 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: 12
- eval_batch_size: 12
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3029 | 1.0 | 1430 | 0.1884 | 0.8237 |
| 0.1573 | 2.0 | 2860 | 0.1770 | 0.8473 |
| 0.0959 | 3.0 | 4290 | 0.1774 | 0.8594 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jgriffi/xlm-roberta-base-finetuned-panx-fr | 77efa863f71b3be7628bb3a4bc667b460afd6cca | 2022-06-04T00:16:41.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jgriffi | null | jgriffi/xlm-roberta-base-finetuned-panx-fr | 1 | null | transformers | 32,613 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.9320766980825479
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0994
- F1: 0.9321
## 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: 12
- eval_batch_size: 12
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5314 | 1.0 | 382 | 0.2522 | 0.8277 |
| 0.2555 | 2.0 | 764 | 0.1414 | 0.9059 |
| 0.1667 | 3.0 | 1146 | 0.0994 | 0.9321 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jgriffi/xlm-roberta-base-finetuned-panx-it | 825bfb8f0a1e25e937a2a796c440ba49127eb7c3 | 2022-06-04T00:32:43.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jgriffi | null | jgriffi/xlm-roberta-base-finetuned-panx-it | 1 | null | transformers | 32,614 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8374017376913528
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2556
- F1: 0.8374
## 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: 12
- eval_batch_size: 12
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6559 | 1.0 | 140 | 0.2821 | 0.7862 |
| 0.251 | 2.0 | 280 | 0.2658 | 0.8179 |
| 0.1457 | 3.0 | 420 | 0.2556 | 0.8374 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jgriffi/xlm-roberta-base-finetuned-panx-en | 647ca5231ab9968d0bfa37eb973342dc89d6b8e3 | 2022-06-04T00:48:44.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jgriffi | null | jgriffi/xlm-roberta-base-finetuned-panx-en | 1 | null | transformers | 32,615 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7054833239118146
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4218
- F1: 0.7055
## 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: 12
- eval_batch_size: 12
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9596 | 1.0 | 99 | 0.5244 | 0.5827 |
| 0.4403 | 2.0 | 198 | 0.4184 | 0.6764 |
| 0.3253 | 3.0 | 297 | 0.4218 | 0.7055 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jgriffi/xlm-roberta-base-finetuned-panx-all | 5271ebb81eac0ce125100d750299dffb00a7077d | 2022-06-04T01:24:48.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jgriffi | null | jgriffi/xlm-roberta-base-finetuned-panx-all | 1 | null | transformers | 32,616 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1448
- F1: 0.8881
## 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: 12
- eval_batch_size: 12
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3029 | 1.0 | 1669 | 0.2075 | 0.7971 |
| 0.164 | 2.0 | 3338 | 0.1612 | 0.8680 |
| 0.1025 | 3.0 | 5007 | 0.1448 | 0.8881 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/katieoneuro | f6db0e917dcd77ecf62a9394402d9419735d5872 | 2022-06-04T01:31:59.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/katieoneuro | 1 | null | transformers | 32,617 | ---
language: en
thumbnail: http://www.huggingtweets.com/katieoneuro/1654306303616/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1000482851853340672/LhUdoFyk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Katie O'Nell 🧠💻</div>
<div style="text-align: center; font-size: 14px;">@katieoneuro</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Katie O'Nell 🧠💻.
| Data | Katie O'Nell 🧠💻 |
| --- | --- |
| Tweets downloaded | 552 |
| Retweets | 323 |
| Short tweets | 17 |
| Tweets kept | 212 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2umesznv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @katieoneuro's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2evfy6ho) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2evfy6ho/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/katieoneuro')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
LinaR/Prediccion_titulos | 46e265fe6670c40d699583831d2a6a7bffafcf56 | 2022-06-04T04:44:50.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | LinaR | null | LinaR/Prediccion_titulos | 1 | null | transformers | 32,618 | ---
tags:
- generated_from_keras_callback
model-index:
- name: Prediccion_titulos
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prediccion_titulos
Este modelo predice los encabezados de las noticias
## Model description
Este modelo fue entrenado con un Transformador T5 y una base de datos en español
## Intended uses & limitations
More information needed
## Training and evaluation data
Los datos fueron tomado del siguiente dataset de Kaggle : https://www.kaggle.com/datasets/josemamuiz/noticias-laraznpblico, el cual es un conjunto de datos se extrajo de las webs de periódicos españoles
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
newlife/AlQgen | 39f65aa7a9a7a676b9ae57db867e330ce3625454 | 2022-06-04T09:10:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | newlife | null | newlife/AlQgen | 1 | null | transformers | 32,619 | Entry not found |
newlife/openq-generator | 1305312190cc20b9f33f17e7d8d3bed9a11ebb4e | 2022-06-04T11:10:12.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | newlife | null | newlife/openq-generator | 1 | null | transformers | 32,620 | Entry not found |
roshnir/xlmr-finetuned-mlqa-dev-hi | b56c72d891c8c5de3bc39a9035b5da72631f4ff5 | 2022-06-04T09:29:18.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-hi | 1 | null | transformers | 32,621 | Entry not found |
aggtamv/wav2vec_2.0_extra_vocab | d4c03727153155e59cb8f2191a2dc875192aa4a4 | 2022-06-08T08:56:07.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | aggtamv | null | aggtamv/wav2vec_2.0_extra_vocab | 1 | null | transformers | 32,622 | Entry not found |
cutten/wav2vec2-base-timit-demo-google-colab | 6b78313144330c8eca5df59dee12e76f859d9a4e | 2022-06-07T03:35:57.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | cutten | null | cutten/wav2vec2-base-timit-demo-google-colab | 1 | null | transformers | 32,623 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
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: 0.6342
- Wer: 0.5808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 9.1358 | 1.19 | 500 | 3.2710 | 1.0 |
| 3.0499 | 2.38 | 1000 | 1.8976 | 1.0 |
| 1.279 | 3.56 | 1500 | 0.7502 | 0.8228 |
| 0.7953 | 4.75 | 2000 | 0.5914 | 0.7343 |
| 0.6451 | 5.94 | 2500 | 0.6152 | 0.7280 |
| 0.5351 | 7.13 | 3000 | 0.5948 | 0.7041 |
| 0.4633 | 8.31 | 3500 | 0.5585 | 0.6712 |
| 0.4272 | 9.5 | 4000 | 0.5372 | 0.6457 |
| 0.3803 | 10.69 | 4500 | 0.5404 | 0.6402 |
| 0.3462 | 11.88 | 5000 | 0.5862 | 0.6484 |
| 0.3302 | 13.06 | 5500 | 0.5991 | 0.6426 |
| 0.3096 | 14.25 | 6000 | 0.5687 | 0.6287 |
| 0.2839 | 15.44 | 6500 | 0.5798 | 0.6384 |
| 0.2701 | 16.63 | 7000 | 0.5775 | 0.6047 |
| 0.2507 | 17.81 | 7500 | 0.5638 | 0.6065 |
| 0.2376 | 19.0 | 8000 | 0.5937 | 0.6094 |
| 0.2264 | 20.19 | 8500 | 0.5944 | 0.6065 |
| 0.2146 | 21.38 | 9000 | 0.6050 | 0.6122 |
| 0.1947 | 22.57 | 9500 | 0.6283 | 0.5992 |
| 0.1982 | 23.75 | 10000 | 0.6126 | 0.6018 |
| 0.1924 | 24.94 | 10500 | 0.6075 | 0.5962 |
| 0.1855 | 26.13 | 11000 | 0.6344 | 0.5938 |
| 0.1839 | 27.32 | 11500 | 0.6118 | 0.5880 |
| 0.1741 | 28.5 | 12000 | 0.6381 | 0.5878 |
| 0.1726 | 29.69 | 12500 | 0.6342 | 0.5808 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
SaiNikhileshReddy/xlm-roberta-large-finetuned-ner | b25d20bdf5c28aec5b271d20ff80cae6bd0df1a9 | 2022-06-04T18:26:17.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:hi_ner_config",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | SaiNikhileshReddy | null | SaiNikhileshReddy/xlm-roberta-large-finetuned-ner | 1 | null | transformers | 32,624 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- hi_ner_config
model-index:
- name: xlm-roberta-large-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the hi_ner_config dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2329
- eval_precision: 0.7110
- eval_recall: 0.6854
- eval_f1: 0.6980
- eval_accuracy: 0.9332
- eval_runtime: 162.3478
- eval_samples_per_second: 66.9
- eval_steps_per_second: 16.73
- epoch: 2.64
- step: 50198
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/orc_nft | 980231871024b21e0b8849ac2c4947b830200b0e | 2022-06-04T16:13:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/orc_nft | 1 | null | transformers | 32,625 | ---
language: en
thumbnail: http://www.huggingtweets.com/orc_nft/1654359188989/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1510438749154549764/sar63AXD_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ORC.A ⍬</div>
<div style="text-align: center; font-size: 14px;">@orc_nft</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ORC.A ⍬.
| Data | ORC.A ⍬ |
| --- | --- |
| Tweets downloaded | 1675 |
| Retweets | 113 |
| Short tweets | 544 |
| Tweets kept | 1018 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wwc37qkh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @orc_nft's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/debtzj0e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/debtzj0e/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/orc_nft')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
grimar/tp_nlp_Roberta_1E | 9f12fdec291ee41b6982879f1dec3c3e9eed90a9 | 2022-06-04T18:09:33.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | grimar | null | grimar/tp_nlp_Roberta_1E | 1 | null | transformers | 32,626 | Entry not found |
atoivat/distilbert-base-uncased-finetuned-squad | 3eba1a8799d9e852d50693977d4225648f6bd1f5 | 2022-06-04T21:13:36.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | atoivat | null | atoivat/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 32,627 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2086 | 1.0 | 5533 | 1.1565 |
| 0.9515 | 2.0 | 11066 | 1.1225 |
| 0.7478 | 3.0 | 16599 | 1.1504 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/centraldamiku | cddf9545758013175974dea577a522969735af9e | 2022-06-04T18:14:42.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/centraldamiku | 1 | null | transformers | 32,628 | ---
language: en
thumbnail: http://www.huggingtweets.com/centraldamiku/1654366478559/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532142310741495808/VWMuTyjo_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Central da Miku</div>
<div style="text-align: center; font-size: 14px;">@centraldamiku</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Central da Miku.
| Data | Central da Miku |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 348 |
| Short tweets | 801 |
| Tweets kept | 2093 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m8jk5mo9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @centraldamiku's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rp6i3tpo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rp6i3tpo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/centraldamiku')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
NadiaSan/udesa-model-aah-es | a5c0771e02cb291a48c56a61b958237c9d4e1a10 | 2022-06-04T21:05:42.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | NadiaSan | null | NadiaSan/udesa-model-aah-es | 1 | null | transformers | 32,629 | Entry not found |
huggingtweets/tomcooper26-tomncooper | f7007f3313ffe97d52aac3c1691c1e457d4e9434 | 2022-06-04T21:53:08.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/tomcooper26-tomncooper | 1 | null | transformers | 32,630 | ---
language: en
thumbnail: http://www.huggingtweets.com/tomcooper26-tomncooper/1654379583668/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000155926309/6204f6960618d11ff5a7e2b21ae9db03_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/290863981/monkey_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tom Cooper & Tom Cooper</div>
<div style="text-align: center; font-size: 14px;">@tomcooper26-tomncooper</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Tom Cooper & Tom Cooper.
| Data | Tom Cooper | Tom Cooper |
| --- | --- | --- |
| Tweets downloaded | 2092 | 3084 |
| Retweets | 179 | 687 |
| Short tweets | 223 | 59 |
| Tweets kept | 1690 | 2338 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dndifpco/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tomcooper26-tomncooper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/97vltow9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/97vltow9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/tomcooper26-tomncooper')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/thundering165 | 8e8d54b688cb66234b9dccd675ec60d59cb8d204 | 2022-06-05T00:16:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/thundering165 | 1 | null | transformers | 32,631 | ---
language: en
thumbnail: http://www.huggingtweets.com/thundering165/1654388210270/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/636093493207666689/dLDycSCd_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Paul Harvey</div>
<div style="text-align: center; font-size: 14px;">@thundering165</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Paul Harvey.
| Data | Paul Harvey |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 381 |
| Short tweets | 120 |
| Tweets kept | 2747 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uv1udbr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thundering165's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hlf7pk2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hlf7pk2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thundering165')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
SmartPy/mt5-small-finetuned-amazon-en-es | 8c5c5483ff95868225f2c7438f356975d58acd80 | 2022-06-05T13:45:57.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SmartPy | null | SmartPy/mt5-small-finetuned-amazon-en-es | 1 | null | transformers | 32,632 | Entry not found |
huggingtweets/cboldisor | 48d69630aa613907ad0ab1c3f8247bc42bf3cb8b | 2022-06-05T08:48:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/cboldisor | 1 | null | transformers | 32,633 | ---
language: en
thumbnail: http://www.huggingtweets.com/cboldisor/1654418897981/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1272169107077677057/Cpv0UEsW_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Costin Boldisor</div>
<div style="text-align: center; font-size: 14px;">@cboldisor</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Costin Boldisor.
| Data | Costin Boldisor |
| --- | --- |
| Tweets downloaded | 153 |
| Retweets | 1 |
| Short tweets | 8 |
| Tweets kept | 144 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/15uralj1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cboldisor's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kbdt4l8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kbdt4l8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cboldisor')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum | 6f003dea0eb36f15b376ac492f4535e9faca60c1 | 2022-06-05T16:47:06.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:mlsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum | 1 | null | transformers | 32,634 | ---
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mT5_multilingual_XLSum-finetuned-xsum-mlsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- 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. -->
# mT5_multilingual_XLSum-finetuned-xsum-mlsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
TheoMrc/b3_ache_tests | 99236f21d3d3b1b08998705818f21bf47558bc54 | 2022-06-05T23:43:46.000Z | [
"pytorch",
"segformer",
"transformers"
] | null | false | TheoMrc | null | TheoMrc/b3_ache_tests | 1 | null | transformers | 32,635 | Entry not found |
jppaolim/v52_Large | a54a0fc9356cd17a6d65d1f48e9d525a690d7d8a | 2022-06-05T11:04:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jppaolim | null | jppaolim/v52_Large | 1 | null | transformers | 32,636 | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was driving home from work. He had to stop at a gas station. He bought a lottery ticket and bought some tickets. He bought some lottery tickets and played them all. He got really lucky and was won the jack jack jack jack jack prize.
Arthur goes to the beach. Arthur decides to go to the beach. First he has to sit on the sand. Next, he would walk the beach. Finally, he can go to the park. Once they get to the beach, he'll go swimming.
Arthur goes to the beach. Arthur goes to the beach. He is bored while watching the sand. He takes a nap. Finally he goes to the water. He gets a nap and heads home.
Arthur goes to the beach. Arthur decides to go to the beach. He begins to put on his shoes and walk to the beach. Finally he comes home to find his dad with him. He is happy he got to see his dad and his dad were together. He decides to go home to rest.
Arthur goes to the beach. Arthur went to the beach to play volleyball. He was excited to be there playing. After playing, his foot broke his hip. His dad had to take him to the hospital. Luckily, the injury was minimal and he went back to playing.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur decides to go to the beach. He begins his day by going to the beach with his dad. At the beach, he played for his friends and watched a movie. Afterwards, they went to a local bar after the movie. They had a good time.
Arthur goes to the beach. Arthur decided to go to the beach with his friends. They had a fun day there and played volleyball all day. He had a good time playing in the beach. His friend ended up taking him out for dinner that night. He had a nice meal with friends.
Arthur goes to the beach. Arthur was going to the beach on Friday. He decided to take his son to the beach. His son spent the weekend playing. In the afternoon his mom went to the mall to see the ocean. She loved watching the ocean.
Arthur goes to the beach. A friend invited me to go to the beach. I agreed and put on my favorite outfit. It took me forever to find my bathing suit, and it was a lot difficult. The ride to the beach was worth a great day!
Arthur goes to the beach. Arthur decided that he wanted to go to the beach. He went to the beach. After a few hours, he left the beach and went to the water. While swimming he found his mother asleep and was able to see the world. When he woke up, he felt very happy.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur was on vacation in Hawaii. He decided to go to the beach. He rented a car and drove to the beach. He went for an hour relaxing in the water. After the hours, he went home.
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. They drove to the beach and spent the day relaxing and relaxing. When they got home, they decided to play volleyball. Afterwards, they all played volleyball and had a great time. After that, they had a nice dinner and had a wonderful time.
Arthur goes to the beach. Arthur is going to the beach with his family. He decides to take his family and his family to the beach. While there, they watch the ocean and relax. Afterwards, they spent the day playing volleyball. After the sun was over, they headed home.
Arthur goes to the beach. Arthur was going to go to the beach with his friend. They decided to take a road trip to Las Vegas. Once they arrived, the friends began to spend a day relaxing and playing. After a few hours of relaxing, he went home after playing video games. When he got home, his friend let him stay out on the beach.
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. He invited some friends. The friends played volleyball and the football. They had fun. At the end of the day, they all left to go home.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur went to the beach with his family. He played volleyball and had fun playing. The other kids decided to play volleyball. They made fun of him for not being able to play. His dad was happy that he got a chance to go to the beach.
Arthur goes to the beach. Arthur is going to the beach with his dad. His father takes him to a different park. He spends hours playing volleyball. After that, he heads home and relax on his couch. When he gets home, his mom tells him he can't play.
Arthur goes to the beach. Arthur is out on a boat with his friends. He decides to go to the beach. While there, he sees a bunch of people. He spends time talking and playing volleyball. He has fun at the beach all day.
Arthur goes to the beach. Arthur was going to go to the beach with his friends. They wanted to spend time together. He decided to take them to the beach for a swim. When they arrived, he had a blast relaxing on the sand. His friends swam and played until he went home.
Arthur goes to the beach. Arthur had never been to the beach before. He decided to go to the beach with his friends. When they got there, he was amazed by all of the beauty and sea life. He decided that going to the beach would be the most fun he had! After a few hours of fun, he decided to go home.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He decided that he would like to swim. He spent a few hours swimming and playing volleyball. When he got home, he realized that he forgot his sunscreen. He went back to the beach without any more sunblock.
Arthur goes to the beach. Arthur is going to the beach with his friends. He wants to go swimming and play volleyball. His friends decide to take him to a park. The boys spend the day playing volleyball and playing volleyball. After the game, he decides to stay home from school.
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He wanted to spend time with them and play volleyball. His friends told him that it would be a good idea to go. The day of the game, Arthur decided to go to the beach. After playing for his friends, he went home.
Arthur goes to the beach. Arthur is going to go to the beach with his family. He wants to spend time playing volleyball but he doesn't have enough money. He decides to get a job and earn money by working hard at his local mall. He begins to work as a mechanic and gets paid for it. He goes home and plays volleyball every day.
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He decided to go by himself. When he got there, everyone said that he was too hot. The weather was so cold that he had to leave. After that day, Arthur went home and watched tv instead of going.
|
Rgl73/xlm-roberta-base-finetuned-panx-de | 296915dada7c8c36c28103494726500ccb26d1fe | 2022-06-05T15:51:50.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Rgl73 | null | Rgl73/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 32,637 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8608532209375177
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1446
- F1: 0.8609
## 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2623 | 1.0 | 787 | 0.1756 | 0.8132 |
| 0.1321 | 2.0 | 1574 | 0.1497 | 0.8458 |
| 0.0856 | 3.0 | 2361 | 0.1446 | 0.8609 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
roshnir/xlmr-finetuned-mlqa-dev-ar-hi | 3ff2346662bf566b97b83a10f6b63c8268a578ed | 2022-06-05T12:12:20.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-ar-hi | 1 | null | transformers | 32,638 | Entry not found |
EmileEsmaili/gpt2_pitchfork | 923d3bc9179e51925b4d6eb9f327d280de4e10c0 | 2022-06-05T16:57:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | EmileEsmaili | null | EmileEsmaili/gpt2_pitchfork | 1 | null | transformers | 32,639 | Entry not found |
haritzpuerto/TinyBERT_General_4L_312D-squad | 9a2840bf7ac60a2aeb2ff51493b41c55e7b024c9 | 2022-06-05T13:16:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | haritzpuerto | null | haritzpuerto/TinyBERT_General_4L_312D-squad | 1 | null | transformers | 32,640 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: TinyBERT_General_4L_312D-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. -->
# TinyBERT_General_4L_312D-squad
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the squad dataset.
It achieves the following results on the evaluation set:
- exact_match: 33.301797540208135
- f1: 45.03886349847048
- Loss: 2.5477
## 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: 20
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7498 | 1.0 | 4380 | 2.5477 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
asahi417/lmqg-mt5-small-dequad | f90d0a007c9012751be0f9213729423d22f2da42 | 2022-06-09T10:55:29.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mt5-small-dequad | 1 | null | transformers | 32,641 | Entry not found |
roshnir/xlmr-finetuned-mlqa-dev-zh-hi | 9af30f232dce1f7e803780791b92a80ca0f12213 | 2022-06-05T13:18:56.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-zh-hi | 1 | null | transformers | 32,642 | Entry not found |
jppaolim/v53_Large_AdaMW | ee3c43251d9522d9833f3b6db8b85979645a681b | 2022-06-05T14:20:25.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jppaolim | null | jppaolim/v53_Large_AdaMW | 1 | null | transformers | 32,643 | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was driving his dog on the highway. He felt a breeze approaching him. He took his dog out and let her out. The dog jumped and got lost. Arthur found her under a tree for her.
Arthur goes to the beach. Arthur decides to go to the beach. First he has to go to the sand. Then he has to walk the beach. Finally he will swim in the ocean. Finally he will swim in the ocean.
Arthur goes to the beach. Arthur is the type of person. He wanted to be the one who didn't want to be alone. He joined the swim team in high school. They invited him to swim in a bikini race. He didn't feel comfortable that he was alone.
Arthur goes to the beach. Arthur is going to the beach. He heads to the ocean. When he gets to the beach he realizes he forgot his sunscreen. He gets in big trouble for being late. The police write him up.
Arthur goes to the beach. Arthur was so sad. The weather was so cold and cold. He took his friends over to watch a movie. After the movie they played volleyball. Arthur was happy that his friends were there to play.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur had always wanted to see the beach. He took his girlfriend out and drove to the beach. He parked his car in the parking lot. Arthur looked outside and saw that the beach was not looking too well. After a couple of hours the car had rolled into a hole.
Arthur goes to the beach. Arthur was going to the beach to relax on the beach. He packed his towel and his sunblock. After the sunbathes, Arthur got a tan. He felt much better afterwards. He spent the next day tanning every day with his friends.
Arthur goes to the beach. Arthur was about to leave for work. It was a cloudy day outside, but he decided to go into town. He drove home and watched the ocean. Suddenly, a big wave wave hit his car in the road. He didn't get out until days later.
Arthur goes to the beach. Arthur is an old man who always wants to go to a beach. He takes some of his friends and family go. He gets his family to drive to the beach. They drive to the beach. He goes to the beach.
Arthur goes to the beach. Arthur went to the beach with his friend. They decided to go into a surf park. Once at the beach he had fun swimming. He decided to spend the rest of the day surfing and surfing. His day was wonderful.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is a very lonely man. He decides he wants to meet someone who is nice to him. He finds a man with a beautiful young woman. The two men fall in love and start to date. They realize they are best friends.
Arthur goes to the beach. Arthur always wanted to see the ocean. His dad told him that it was too hot. One day Arthur asked his friend Jack if he could take her. Jack agreed to come with him and they drove to the beach. They had fun relaxing in the ocean.
Arthur goes to the beach. Arthur is going to go swimming. He has never been to a beach before. Arthur decides to take his friends out to the beach. He takes his friend out to the beach and spends time on the water. Arthur feels glad he went to the beach.
Arthur goes to the beach. Arthur is going to the beach. He decides he should go to the beach with his friends. He arrives at the beach with his friends. They spend the day playing volleyball. The kids all go home happy.
Arthur goes to the beach. Arthur is very adventurous. He decides to go to the beach. He heads to the beach with his friends. He spends two days relaxing on the beach. He is happy that he got to spend time relaxing on the beach.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur is on vacation in Hawaii. He decides to go to the beach with his friends. At the beach he meets a nice girl named Courtney. The two of them meet up for a date and get married. Now they are both happy that he went to the beach.
Arthur goes to the beach. Arthur is a very adventurous person. He wants to go to the beach with his friends. They decide to go to the beach to relax. Then, they spend the day relaxing on the beach. Finally, he is able to relax on the beach.
Arthur goes to the beach. Arthur was very excited for a relaxing day at the beach. He took his family and friends out on the water. They swam and swam all day long. It was a relaxing day that made him happy. The beach was so relaxing he couldn't be happier.
Arthur goes to the beach. Arthur was going to the beach with his family. He had never seen a water park before. They arrived at the beach and started to explore. It was a peaceful day. Afterwards, he decided to go swimming with his friends.
Arthur goes to the beach. Arthur was going to go to the beach. He had never seen one before. He decided to drive to the beach. He drove for a couple of hours. Finally he arrived at the beach.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is a very adventurous person. He decides that he needs to get out of his house and go for it. He gets out on the beach with friends. They spend hours swimming in the ocean. He feels happy that he went outside.
Arthur goes to the beach. Arthur is going to the beach with his friends. He's going to be in the water for a couple of minutes. When he gets out, it begins to rainy and thunderstorms. The rain starts to pour down on him. He will not be able to swim until after that.
Arthur goes to the beach. Arthur is going to go to the beach with his friends. He gets a flat tire on the road. His friend comes over and takes him out. The car begins to drive. They all have fun at the beach.
Arthur goes to the beach. Arthur is a very adventurous man who loves swimming in the ocean. He decides that he needs to swim in the ocean at least once per week. At first, he finds it difficult to swim because of his weight. Finally he begins to enjoy the water and relax. After a few weeks, Arthur feels happy that he finally gets to swim.
Arthur goes to the beach. Arthur is going to go to the beach with his friends. They decide to play volleyball. He gets a lot of sunblock on his legs and body. The other team wins by a large margin. It's fun playing basketball in the ocean.
|
meetyildiz/M-TurQA-bert-base-turkish-uncased-finetuned-toqad | 47907aa4b8266ab96fa64903631cac770d721d24 | 2022-06-05T13:43:58.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-bert-base-turkish-uncased-finetuned-toqad | 1 | null | transformers | 32,644 | Entry not found |
meetyildiz/M-TurQA-convbert-base-turkish-cased-finetuned-toqad | 2c7a4ceef042031bbd35289d33fae935b53670d0 | 2022-06-05T13:52:37.000Z | [
"pytorch",
"convbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-convbert-base-turkish-cased-finetuned-toqad | 1 | null | transformers | 32,645 | Entry not found |
meetyildiz/M-TurQA-distilbert-base-turkish-cased-finetuned-toqad | c41820cb057176cead26ccd88d8df5d64f53ea8f | 2022-06-05T14:00:58.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-distilbert-base-turkish-cased-finetuned-toqad | 1 | null | transformers | 32,646 | Entry not found |
meetyildiz/M-TurQA-bert-base-turkish-cased-finetuned-toqad-aug | 784292e471a9caddcf193f32754c8744008ccf6b | 2022-06-05T14:51:38.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-bert-base-turkish-cased-finetuned-toqad-aug | 1 | null | transformers | 32,647 | Entry not found |
erfangc/mt5-small-finetuned-amazon-en-es | 6a464d94333b537d1a12af0f3197e6c094db9c80 | 2022-06-05T16:32:46.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erfangc | null | erfangc/mt5-small-finetuned-amazon-en-es | 1 | null | transformers | 32,648 | Entry not found |
meetyildiz/M-TurQA-xlm-roberta-base-finetuned-toqad-aug | 38925d041915ac208c259a3243089e337ddfaa8f | 2022-06-05T15:09:49.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-xlm-roberta-base-finetuned-toqad-aug | 1 | null | transformers | 32,649 | Entry not found |
meetyildiz/M-TurQA-bert-base-turkish-128k-cased-finetuned-toqad-aug | e85b4cc47f0ce95ef2e2936f76e273c2cfedb4ff | 2022-06-05T15:31:23.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | meetyildiz | null | meetyildiz/M-TurQA-bert-base-turkish-128k-cased-finetuned-toqad-aug | 1 | null | transformers | 32,650 | Entry not found |
Bistolero/nl_GA_32b | 59faff389c1bc228c07cb295f61019193f84390b | 2022-06-05T16:43:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Bistolero | null | Bistolero/nl_GA_32b | 1 | null | transformers | 32,651 | Entry not found |
AllenGeng/OCamlBert | acec8b008f7533942b8fcd145e47c95fb7953e42 | 2022-06-06T18:28:40.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | AllenGeng | null | AllenGeng/OCamlBert | 1 | null | transformers | 32,652 | ---
license: mit
---
|
daianadte/roberta-NLI-DMPV | 9c9265f2008a29e14c965e93fd457954a4880663 | 2022-06-05T21:40:10.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | daianadte | null | daianadte/roberta-NLI-DMPV | 1 | null | transformers | 32,653 | Entry not found |
asahi417/lmqg-mt5-small-itquad | 48748708e370e34cdb11bb07fbe003c7f0f37997 | 2022-06-09T10:59:30.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mt5-small-itquad | 1 | null | transformers | 32,654 | Entry not found |
erfangc/mt5-small-sandbox1 | 4d7d6e36e31ef6e42f6d9982261d64925624a59f | 2022-06-06T03:10:37.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | erfangc | null | erfangc/mt5-small-sandbox1 | 1 | null | transformers | 32,655 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-sandbox1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-sandbox1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 14.5875
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.10.3
|
joshanashakya/old_mini_codebert_sourcecode_nmt_pn2ja_200E_5e-05LR | 318160abee826808b3eea0b21add261cbd6351f9 | 2022-06-06T04:34:26.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/old_mini_codebert_sourcecode_nmt_pn2ja_200E_5e-05LR | 1 | null | transformers | 32,656 | Entry not found |
eunjin/kobart_jeju_translator | e4b8349584f1031ffcc3d0fc12c5e6c223706e48 | 2022-06-06T13:44:49.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | eunjin | null | eunjin/kobart_jeju_translator | 1 | null | transformers | 32,657 | Korean Dialect Translator: Standard > Jeju
- Used Data : AI hub 한국어 방언 발화(제주도)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation
|
eunjin/kobart_jeju_to_standard_translator | e2c24189d441f0ac939ad2ffc0d0df4163914830 | 2022-06-06T13:44:31.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | eunjin | null | eunjin/kobart_jeju_to_standard_translator | 1 | null | transformers | 32,658 | Korean Dialect Translator: Jeju > Standard
- Used Data : AI hub 한국어 방언 발화(제주도)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation
|
eunjin/kobart_gyeongsang_to_standard_translator | c211f446be3858027978791dd70e0fe23caad1b9 | 2022-06-06T13:44:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | eunjin | null | eunjin/kobart_gyeongsang_to_standard_translator | 1 | null | transformers | 32,659 | Korean Dialect Translator: Gyeongsang > Standard
- Used Data : AI hub 한국어 방언 발화(경상도)
- Used Model : SKT-KoBART
- https://github.com/SKT-AI/KoBART
- Reference Code
- https://github.com/seujung/KoBART-translation |
joshanashakya/old_codebert_sourcecode_nmt_ja2pn_50E_5e-05LR | eaf1851c514a5b799ba077ebd1015cda08ffab0d | 2022-06-06T07:07:17.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/old_codebert_sourcecode_nmt_ja2pn_50E_5e-05LR | 1 | null | transformers | 32,660 | Entry not found |
botika/distilbert-base-uncased-finetuned-squad | 8c11a47cf871eeee5184e189f2ec150ad8250f30 | 2022-06-07T06:36:08.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | botika | null | botika/distilbert-base-uncased-finetuned-squad | 1 | null | transformers | 32,661 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3149 | 1.0 | 2767 | 1.2079 |
| 1.053 | 2.0 | 5534 | 1.1408 |
| 0.8809 | 3.0 | 8301 | 1.1500 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
VRT/mT5_summarization | bcc20058413439d84bc8d3e59c4deb30517831c2 | 2022-06-06T10:17:46.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | VRT | null | VRT/mT5_summarization | 1 | null | transformers | 32,662 | Entry not found |
eunbeee/ainize-kobart-news-eb-finetuned-xsum | cd0990000c901d7f9e68edd0b459b95b8cf852a7 | 2022-06-08T08:34:26.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | eunbeee | null | eunbeee/ainize-kobart-news-eb-finetuned-xsum | 1 | null | transformers | 32,663 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ainize-kobart-news-eb-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ainize-kobart-news-eb-finetuned-xsum
This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2147
- Rouge1: 60.732
- Rouge2: 39.1933
- Rougel: 60.6507
- Rougelsum: 60.6712
- Gen Len: 19.3417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0649 | 1.0 | 749 | 0.5502 | 56.6571 | 36.5992 | 56.6185 | 56.6364 | 19.2929 |
| 0.7103 | 2.0 | 1498 | 0.3904 | 59.1212 | 38.3611 | 59.093 | 59.1191 | 19.31 |
| 0.4723 | 3.0 | 2247 | 0.2922 | 60.1133 | 38.7819 | 60.0439 | 60.0572 | 19.2659 |
| 0.3841 | 4.0 | 2996 | 0.2367 | 60.4405 | 39.0176 | 60.366 | 60.4057 | 19.3397 |
| 0.3091 | 5.0 | 3745 | 0.2147 | 60.732 | 39.1933 | 60.6507 | 60.6712 | 19.3417 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
spy24/autotrain-expand-928531583 | 05bed7971d189e0c0293a8db47cfce52ac1f6902 | 2022-06-06T16:04:02.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:spy24/autotrain-data-expand",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | spy24 | null | spy24/autotrain-expand-928531583 | 1 | 1 | transformers | 32,664 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- spy24/autotrain-data-expand
co2_eq_emissions: 3.4552892403407167
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 928531583
- CO2 Emissions (in grams): 3.4552892403407167
## Validation Metrics
- Loss: 2.1122372150421143
- Rouge1: 68.7226
- Rouge2: 50.1638
- RougeL: 59.7235
- RougeLsum: 62.3458
- Gen Len: 63.2505
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/spy24/autotrain-expand-928531583
``` |
jontooy/AraBERT256-Flickr8k | ca2a9af0752584e24e2d4aca3ce6d8891117aa82 | 2022-06-06T12:21:07.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | fill-mask | false | jontooy | null | jontooy/AraBERT256-Flickr8k | 1 | null | transformers | 32,665 | ---
license: afl-3.0
---
|
jontooy/GigaBERT32-COCO | 30d1a9754ea211b973145879367bceb631452633 | 2022-06-06T12:25:18.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:afl-3.0"
] | feature-extraction | false | jontooy | null | jontooy/GigaBERT32-COCO | 1 | null | transformers | 32,666 | ---
license: afl-3.0
---
|
VRT/mT5_initial | a1b3fe9bfbce847415d99d5a6140369e629cdd74 | 2022-06-06T14:31:41.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | VRT | null | VRT/mT5_initial | 1 | null | transformers | 32,667 | Entry not found |
stopdoingmath/opus-mt-sla-en-finetuned-uk-to-en | 96fddf47fc22e7ee19b9b2deda6f9c6e9ccdd2bc | 2022-06-06T17:20:17.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:opus100",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | stopdoingmath | null | stopdoingmath/opus-mt-sla-en-finetuned-uk-to-en | 1 | null | transformers | 32,668 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-sla-en-finetuned-uk-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: default
metrics:
- name: Bleu
type: bleu
value: 27.7684
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-sla-en-finetuned-uk-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Helsinki-NLP/opus-mt-sla-en) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7232
- Bleu: 27.7684
- Gen Len: 12.2485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.5284 | 1.0 | 62500 | 1.7232 | 27.7684 | 12.2485 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/russellriesjr | e9623f17e00fddbb9b2228dbde1f5e7d89846582 | 2022-06-06T18:54:21.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/russellriesjr | 1 | null | transformers | 32,669 | ---
language: en
thumbnail: http://www.huggingtweets.com/russellriesjr/1654541578565/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468670117357789192/sStrLB1i_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Russell Ries Jr.</div>
<div style="text-align: center; font-size: 14px;">@russellriesjr</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Russell Ries Jr..
| Data | Russell Ries Jr. |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 2163 |
| Short tweets | 135 |
| Tweets kept | 938 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hn2gsci/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @russellriesjr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/378xjgs6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/378xjgs6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/russellriesjr')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
erickfm/t5-small-finetuned-bias-sweep | 8a9440892900fd941788a399a34f3cb50bb745ee | 2022-06-07T03:47:56.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep | 1 | null | transformers | 32,670 | Entry not found |
asahi417/lmqg-mt5-small-ruquad | 572fbfbfdc9a3dce565f1ab441538802d55ac7a8 | 2022-06-09T10:51:26.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mt5-small-ruquad | 1 | null | transformers | 32,671 | Entry not found |
huggingtweets/hopedavistweets | f0e47b6abbd2a7770e0996308bc86dca2ac0e4a0 | 2022-06-07T00:48:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/hopedavistweets | 1 | null | transformers | 32,672 | ---
language: en
thumbnail: http://www.huggingtweets.com/hopedavistweets/1654562883505/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1420954294082326529/ZkxWu0ln_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Hope Davis 🪩</div>
<div style="text-align: center; font-size: 14px;">@hopedavistweets</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Hope Davis 🪩.
| Data | Hope Davis 🪩 |
| --- | --- |
| Tweets downloaded | 2707 |
| Retweets | 1812 |
| Short tweets | 100 |
| Tweets kept | 795 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pkx13m4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hopedavistweets's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/objxokv4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/objxokv4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/hopedavistweets')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/sofiaazeman | 7e84bdc63861225acadad7085974b9efb04e4d34 | 2022-06-07T00:53:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/sofiaazeman | 1 | null | transformers | 32,673 | ---
language: en
thumbnail: http://www.huggingtweets.com/sofiaazeman/1654563180290/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511483454495637510/BWEFnW4O_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sofi Zeman</div>
<div style="text-align: center; font-size: 14px;">@sofiaazeman</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Sofi Zeman.
| Data | Sofi Zeman |
| --- | --- |
| Tweets downloaded | 317 |
| Retweets | 158 |
| Short tweets | 26 |
| Tweets kept | 133 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uxm4ug9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sofiaazeman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6819mjpo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6819mjpo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sofiaazeman')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/sophiadonis10 | c3427af7857c496940f60b69cf5d7cdc4f9691ff | 2022-06-07T01:01:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/sophiadonis10 | 1 | null | transformers | 32,674 | ---
language: en
thumbnail: http://www.huggingtweets.com/sophiadonis10/1654563613795/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1475251222802309123/0V1B7h3p_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sophia Donis</div>
<div style="text-align: center; font-size: 14px;">@sophiadonis10</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Sophia Donis.
| Data | Sophia Donis |
| --- | --- |
| Tweets downloaded | 320 |
| Retweets | 113 |
| Short tweets | 5 |
| Tweets kept | 202 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4gt337he/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sophiadonis10's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u0jynrk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u0jynrk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sophiadonis10')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Nithiwat/wangchanberta-base-att-spm-uncased-finetuned-imdb | 856e6975414b14881020ae575a71e868da2385f1 | 2022-06-07T01:25:53.000Z | [
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | Nithiwat | null | Nithiwat/wangchanberta-base-att-spm-uncased-finetuned-imdb | 1 | null | transformers | 32,675 | ---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5910
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9341 | 1.0 | 295 | 2.6511 |
| 2.8093 | 2.0 | 590 | 2.6178 |
| 2.7689 | 3.0 | 885 | 2.5321 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
dmpv/siamesa-concat-dmpv | ac222fa460890da6e82be20a8a92074001b2c896 | 2022-06-07T01:47:45.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmpv | null | dmpv/siamesa-concat-dmpv | 1 | null | transformers | 32,676 | Entry not found |
erickfm/t5-base-finetuned-bias-v7 | a2b396607dcb8ed40b1c52c673ccbd252006b1c2 | 2022-06-07T02:56:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-base-finetuned-bias-v7 | 1 | null | transformers | 32,677 | Entry not found |
SmartPy/fine-tuned-t5-small-accelerate | 378102d795864e0ae310d17c909c78aeb43a69ca | 2022-06-07T06:40:32.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SmartPy | null | SmartPy/fine-tuned-t5-small-accelerate | 1 | 1 | transformers | 32,678 | Entry not found |
nestoralvaro/mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base | d718b324c78f0d0b729687262e8fe9c784235cfd | 2022-06-07T09:56:14.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:mlsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base | 1 | null | transformers | 32,679 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 0.1582
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.1582
- Rouge2: 0.0133
- Rougel: 0.1585
- Rougelsum: 0.1586
- Gen Len: 10.2326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 0.1582 | 0.0133 | 0.1585 | 0.1586 | 10.2326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Keerthana/wav2vec2-large-xls-r-300m-ta-colab | 508b62985394394910b452c0256e6123b980c634 | 2022-06-09T13:39:44.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Keerthana | null | Keerthana/wav2vec2-large-xls-r-300m-ta-colab | 1 | null | transformers | 32,680 | Entry not found |
anjankumar/mbart-large-50-finetuned-en-to-te | 508cfb05aece7d4db2f3801a7d1f91026a491089 | 2022-06-19T16:32:07.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:kde4",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | anjankumar | null | anjankumar/mbart-large-50-finetuned-en-to-te | 1 | null | transformers | 32,681 | ---
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: mbart-large-50-finetuned-en-to-te
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-te
metrics:
- name: Bleu
type: bleu
value: 0.7152
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-50-finetuned-en-to-te
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 13.8521
- Bleu: 0.7152
- Gen Len: 20.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 7 | 13.8521 | 0.7152 | 20.5 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
twieland/VN_ja-en_helsinki | c21b586f12c85f8df24be87a29749e7d5a81ee75 | 2022-06-07T08:55:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | twieland | null | twieland/VN_ja-en_helsinki | 1 | null | transformers | 32,682 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: VN_ja-en_helsinki
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. -->
# VN_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2409
- BLEU: 15.28
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6165 | 0.19 | 2000 | 2.6734 |
| 2.3805 | 0.39 | 4000 | 2.6047 |
| 2.2793 | 0.58 | 6000 | 2.5461 |
| 2.2028 | 0.78 | 8000 | 2.5127 |
| 2.1361 | 0.97 | 10000 | 2.4511 |
| 1.9653 | 1.17 | 12000 | 2.4331 |
| 1.934 | 1.36 | 14000 | 2.3840 |
| 1.9002 | 1.56 | 16000 | 2.3901 |
| 1.87 | 1.75 | 18000 | 2.3508 |
| 1.8408 | 1.95 | 20000 | 2.3082 |
| 1.6937 | 2.14 | 22000 | 2.3279 |
| 1.6371 | 2.34 | 24000 | 2.3052 |
| 1.6264 | 2.53 | 26000 | 2.3071 |
| 1.6029 | 2.72 | 28000 | 2.2685 |
| 1.5847 | 2.92 | 30000 | 2.2409 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erickfm/t5-small-finetuned-bias-sweep-2302e2a8 | cd378db3a779c4b2217ddcbafcc7f04f236dc44e | 2022-06-07T07:41:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-2302e2a8 | 1 | null | transformers | 32,683 | Entry not found |
erickfm/t5-small-finetuned-bias-sweep-6ca1c8f4 | eee00ba998620a06648f6d406a0943640e955c41 | 2022-06-07T08:43:12.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-6ca1c8f4 | 1 | null | transformers | 32,684 | Entry not found |
twieland/LN_ja-en_helsinki | 1d1c529468ea6a9c60ed95912aa279481e24998c | 2022-06-07T22:34:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | twieland | null | twieland/LN_ja-en_helsinki | 1 | null | transformers | 32,685 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: LN_ja-en_helsinki
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. -->
# LN_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5382
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.5108 | 0.02 | 2000 | 2.8405 |
| 2.2729 | 0.04 | 4000 | 2.7758 |
| 2.1673 | 0.06 | 6000 | 2.7098 |
| 2.0981 | 0.08 | 8000 | 2.6487 |
| 2.048 | 0.1 | 10000 | 2.7008 |
| 2.0077 | 0.12 | 12000 | 2.6614 |
| 1.9677 | 0.13 | 14000 | 2.6227 |
| 1.9445 | 0.15 | 16000 | 2.5895 |
| 1.9137 | 0.17 | 18000 | 2.5897 |
| 1.8911 | 0.19 | 20000 | 2.6771 |
| 1.8695 | 0.21 | 22000 | 2.6332 |
| 1.8479 | 0.23 | 24000 | 2.6130 |
| 1.8378 | 0.25 | 26000 | 2.6518 |
| 1.8191 | 0.27 | 28000 | 2.6401 |
| 1.8024 | 0.29 | 30000 | 2.6617 |
| 1.7933 | 0.31 | 32000 | 2.6705 |
| 1.7787 | 0.33 | 34000 | 2.6280 |
| 1.7661 | 0.35 | 36000 | 2.6911 |
| 1.7514 | 0.36 | 38000 | 2.6766 |
| 1.7444 | 0.38 | 40000 | 2.6996 |
| 1.7363 | 0.4 | 42000 | 2.6276 |
| 1.722 | 0.42 | 44000 | 2.6466 |
| 1.7177 | 0.44 | 46000 | 2.5937 |
| 1.7055 | 0.46 | 48000 | 2.6386 |
| 1.6956 | 0.48 | 50000 | 2.6794 |
| 1.6885 | 0.5 | 52000 | 2.7336 |
| 1.679 | 0.52 | 54000 | 2.7266 |
| 1.6715 | 0.54 | 56000 | 2.6945 |
| 1.6666 | 0.56 | 58000 | 2.7111 |
| 1.6599 | 0.58 | 60000 | 2.7205 |
| 1.6566 | 0.59 | 62000 | 2.7194 |
| 1.6481 | 0.61 | 64000 | 2.6582 |
| 1.6434 | 0.63 | 66000 | 2.6859 |
| 1.6315 | 0.65 | 68000 | 2.7058 |
| 1.6258 | 0.67 | 70000 | 2.7428 |
| 1.6189 | 0.69 | 72000 | 2.7411 |
| 1.6169 | 0.71 | 74000 | 2.7039 |
| 1.6087 | 0.73 | 76000 | 2.6844 |
| 1.6021 | 0.75 | 78000 | 2.6454 |
| 1.6034 | 0.77 | 80000 | 2.6596 |
| 1.5941 | 0.79 | 82000 | 2.6903 |
| 1.5862 | 0.81 | 84000 | 2.7099 |
| 1.5836 | 0.83 | 86000 | 2.6929 |
| 1.5827 | 0.84 | 88000 | 2.7181 |
| 1.5747 | 0.86 | 90000 | 2.6888 |
| 1.5678 | 0.88 | 92000 | 2.6662 |
| 1.5643 | 0.9 | 94000 | 2.6663 |
| 1.561 | 0.92 | 96000 | 2.6699 |
| 1.5565 | 0.94 | 98000 | 2.6667 |
| 1.5501 | 0.96 | 100000 | 2.6828 |
| 1.5476 | 0.98 | 102000 | 2.6531 |
| 1.5444 | 1.0 | 104000 | 2.6799 |
| 1.5057 | 1.02 | 106000 | 2.6525 |
| 1.5003 | 1.04 | 108000 | 2.6996 |
| 1.4996 | 1.06 | 110000 | 2.6649 |
| 1.4996 | 1.07 | 112000 | 2.6974 |
| 1.4966 | 1.09 | 114000 | 2.7594 |
| 1.4967 | 1.11 | 116000 | 2.6966 |
| 1.492 | 1.13 | 118000 | 2.6929 |
| 1.4923 | 1.15 | 120000 | 2.6522 |
| 1.4838 | 1.17 | 122000 | 2.6363 |
| 1.4839 | 1.19 | 124000 | 2.6849 |
| 1.4807 | 1.21 | 126000 | 2.6667 |
| 1.4778 | 1.23 | 128000 | 2.6684 |
| 1.4731 | 1.25 | 130000 | 2.6338 |
| 1.4727 | 1.27 | 132000 | 2.6093 |
| 1.4695 | 1.29 | 134000 | 2.6020 |
| 1.4656 | 1.3 | 136000 | 2.6341 |
| 1.4648 | 1.32 | 138000 | 2.6509 |
| 1.4578 | 1.34 | 140000 | 2.6807 |
| 1.4606 | 1.36 | 142000 | 2.6357 |
| 1.4529 | 1.38 | 144000 | 2.6404 |
| 1.4488 | 1.4 | 146000 | 2.6347 |
| 1.4442 | 1.42 | 148000 | 2.6058 |
| 1.4447 | 1.44 | 150000 | 2.6645 |
| 1.4432 | 1.46 | 152000 | 2.6070 |
| 1.437 | 1.48 | 154000 | 2.5987 |
| 1.4345 | 1.5 | 156000 | 2.6309 |
| 1.43 | 1.52 | 158000 | 2.5947 |
| 1.4301 | 1.54 | 160000 | 2.5938 |
| 1.4267 | 1.55 | 162000 | 2.6146 |
| 1.426 | 1.57 | 164000 | 2.6519 |
| 1.4193 | 1.59 | 166000 | 2.6163 |
| 1.416 | 1.61 | 168000 | 2.5793 |
| 1.4146 | 1.63 | 170000 | 2.6031 |
| 1.4091 | 1.65 | 172000 | 2.5826 |
| 1.4067 | 1.67 | 174000 | 2.5891 |
| 1.4081 | 1.69 | 176000 | 2.6006 |
| 1.4023 | 1.71 | 178000 | 2.5697 |
| 1.4003 | 1.73 | 180000 | 2.5633 |
| 1.3986 | 1.75 | 182000 | 2.5494 |
| 1.3924 | 1.77 | 184000 | 2.5577 |
| 1.3931 | 1.78 | 186000 | 2.5888 |
| 1.3851 | 1.8 | 188000 | 2.5716 |
| 1.3869 | 1.82 | 190000 | 2.5570 |
| 1.3825 | 1.84 | 192000 | 2.5702 |
| 1.3787 | 1.86 | 194000 | 2.5754 |
| 1.3738 | 1.88 | 196000 | 2.5901 |
| 1.3734 | 1.9 | 198000 | 2.5374 |
| 1.3693 | 1.92 | 200000 | 2.5897 |
| 1.3703 | 1.94 | 202000 | 2.5422 |
| 1.3685 | 1.96 | 204000 | 2.5825 |
| 1.3664 | 1.98 | 206000 | 2.5201 |
| 1.3607 | 2.0 | 208000 | 2.5733 |
| 1.3217 | 2.02 | 210000 | 2.5879 |
| 1.31 | 2.03 | 212000 | 2.5777 |
| 1.3125 | 2.05 | 214000 | 2.5724 |
| 1.3084 | 2.07 | 216000 | 2.5968 |
| 1.3087 | 2.09 | 218000 | 2.5976 |
| 1.3063 | 2.11 | 220000 | 2.5969 |
| 1.3057 | 2.13 | 222000 | 2.6353 |
| 1.3067 | 2.15 | 224000 | 2.6147 |
| 1.3013 | 2.17 | 226000 | 2.5897 |
| 1.3018 | 2.19 | 228000 | 2.5783 |
| 1.2968 | 2.21 | 230000 | 2.6172 |
| 1.2975 | 2.23 | 232000 | 2.6180 |
| 1.2946 | 2.25 | 234000 | 2.6192 |
| 1.299 | 2.26 | 236000 | 2.5895 |
| 1.2896 | 2.28 | 238000 | 2.5682 |
| 1.287 | 2.3 | 240000 | 2.5653 |
| 1.2902 | 2.32 | 242000 | 2.5501 |
| 1.2862 | 2.34 | 244000 | 2.5747 |
| 1.2841 | 2.36 | 246000 | 2.5654 |
| 1.2838 | 2.38 | 248000 | 2.5703 |
| 1.2813 | 2.4 | 250000 | 2.5919 |
| 1.2778 | 2.42 | 252000 | 2.5552 |
| 1.2821 | 2.44 | 254000 | 2.5603 |
| 1.2729 | 2.46 | 256000 | 2.5455 |
| 1.2718 | 2.48 | 258000 | 2.5688 |
| 1.2729 | 2.49 | 260000 | 2.5574 |
| 1.2699 | 2.51 | 262000 | 2.5468 |
| 1.2677 | 2.53 | 264000 | 2.5704 |
| 1.2647 | 2.55 | 266000 | 2.5665 |
| 1.2628 | 2.57 | 268000 | 2.5594 |
| 1.2636 | 2.59 | 270000 | 2.5426 |
| 1.2573 | 2.61 | 272000 | 2.5666 |
| 1.2576 | 2.63 | 274000 | 2.5580 |
| 1.2511 | 2.65 | 276000 | 2.5742 |
| 1.2513 | 2.67 | 278000 | 2.5646 |
| 1.2495 | 2.69 | 280000 | 2.5669 |
| 1.2472 | 2.71 | 282000 | 2.5700 |
| 1.2478 | 2.73 | 284000 | 2.5496 |
| 1.2471 | 2.74 | 286000 | 2.5335 |
| 1.2436 | 2.76 | 288000 | 2.5315 |
| 1.2411 | 2.78 | 290000 | 2.5302 |
| 1.2391 | 2.8 | 292000 | 2.5290 |
| 1.2352 | 2.82 | 294000 | 2.5303 |
| 1.2332 | 2.84 | 296000 | 2.5412 |
| 1.233 | 2.86 | 298000 | 2.5523 |
| 1.2298 | 2.88 | 300000 | 2.5524 |
| 1.2285 | 2.9 | 302000 | 2.5517 |
| 1.2297 | 2.92 | 304000 | 2.5419 |
| 1.2256 | 2.94 | 306000 | 2.5404 |
| 1.2239 | 2.96 | 308000 | 2.5390 |
| 1.2264 | 2.97 | 310000 | 2.5364 |
| 1.2259 | 2.99 | 312000 | 2.5382 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erickfm/t5-small-finetuned-bias-sweep-18dcbe1c | 295a8d6071e086e3fe670e7fc26bdd3f566b419a | 2022-06-07T09:14:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-18dcbe1c | 1 | null | transformers | 32,686 | Entry not found |
prashanth/IndicBART-ibart-hi-to-en | 56bb36870ea59b6674124f6235365420cc3df5e9 | 2022-06-07T09:33:58.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:hindi_english_machine_translation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | prashanth | null | prashanth/IndicBART-ibart-hi-to-en | 1 | null | transformers | 32,687 | ---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
model-index:
- name: IndicBART-ibart-hi-to-en
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. -->
# IndicBART-ibart-hi-to-en
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.4208 | 1.0626 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
ishansharma1320/wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0 | d9e503a145087c3fae94085541561ce307db8729 | 2022-06-07T20:08:08.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ishansharma1320 | null | ishansharma1320/wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0 | 1 | null | transformers | 32,688 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
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: 0.7392
- Wer: 1.0141
## 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: 4.42184e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2217 | 3.03 | 400 | 4.0314 | 1.0 |
| 3.2902 | 6.06 | 800 | 2.1356 | 1.0001 |
| 0.9858 | 9.09 | 1200 | 0.8566 | 1.0037 |
| 0.5131 | 12.12 | 1600 | 0.7481 | 1.0074 |
| 0.3781 | 15.15 | 2000 | 0.7437 | 1.008 |
| 0.2998 | 18.18 | 2400 | 0.7310 | 1.0162 |
| 0.2553 | 21.21 | 2800 | 0.7384 | 1.0159 |
| 0.2216 | 24.24 | 3200 | 0.7537 | 1.0100 |
| 0.2048 | 27.27 | 3600 | 0.7392 | 1.0141 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
erickfm/t5-small-finetuned-bias-sweep-ddee5fc3 | bbe6f1e4e93b8c15bc95d499763284a941ecd81f | 2022-06-07T11:40:11.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-ddee5fc3 | 1 | null | transformers | 32,689 | Entry not found |
xfbai/AMRBART-base-finetuned-AMR2.0-AMRParsing | 4c27652746c9ea5125332707d971b20ea57eee32 | 2022-06-07T12:21:05.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | xfbai | null | xfbai/AMRBART-base-finetuned-AMR2.0-AMRParsing | 1 | null | transformers | 32,690 | ---
license: mit
---
|
xfbai/AMRBART-base-finetuned-AMR3.0-AMRParsing | 9f9dcbf75fa7eebea5203856481a5f6ab05e21f5 | 2022-06-07T12:52:21.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | xfbai | null | xfbai/AMRBART-base-finetuned-AMR3.0-AMRParsing | 1 | null | transformers | 32,691 | ---
license: mit
---
|
erickfm/t5-small-finetuned-bias-sweep-1436a0b1 | a63de9ba13a86e3cf12627f499afbc2984f03397 | 2022-06-07T12:56:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-1436a0b1 | 1 | null | transformers | 32,692 | Entry not found |
erickfm/t5-small-finetuned-bias-sweep-d74f666c | 5195831bb5dfe47b1a91b5f1021fdb1167ae4bc5 | 2022-06-07T13:20:27.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-d74f666c | 1 | null | transformers | 32,693 | Entry not found |
EthanChen0418/task2_macbert_multi_class | 56a40e5b839292639a62de1b29404327c9087a70 | 2022-06-07T14:05:50.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | EthanChen0418 | null | EthanChen0418/task2_macbert_multi_class | 1 | null | transformers | 32,694 | Entry not found |
enoriega/rule_learning_margin_test | c4ab5569aca5ca6b5d775231b88d1ea173bb1da7 | 2022-06-08T05:00:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"dataset:enoriega/odinsynth_dataset",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false | enoriega | null | enoriega/rule_learning_margin_test | 1 | null | transformers | 32,695 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_test
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. -->
# rule_learning_margin_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6468 | 0.32 | 20 | 0.6191 |
| 0.5185 | 0.64 | 40 | 0.5083 |
| 0.459 | 0.96 | 60 | 0.4521 |
| 0.4352 | 1.29 | 80 | 0.4192 |
| 0.4427 | 1.61 | 100 | 0.4199 |
| 0.4246 | 1.93 | 120 | 0.4131 |
| 0.4301 | 2.26 | 140 | 0.4104 |
| 0.428 | 2.58 | 160 | 0.4099 |
| 0.4161 | 2.9 | 180 | 0.4102 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
brindap/wav2vec2-large-xls-r-300m-ta-colab | 6035ea0a7221d174e10f9ddbfca632133e0f9dea | 2022-06-12T06:38:33.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | brindap | null | brindap/wav2vec2-large-xls-r-300m-ta-colab | 1 | null | transformers | 32,696 | Entry not found |
erickfm/t5-small-finetuned-bias-sweep-ec6be410 | 892784c992d373a3aeb06d2ed81325290fa6b6c4 | 2022-06-07T17:07:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-ec6be410 | 1 | null | transformers | 32,697 | Entry not found |
erickfm/t5-small-finetuned-bias-sweep-692e0c16 | 092dc0ce1b62de56e9378126daa52c8b781537ef | 2022-06-07T18:27:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-692e0c16 | 1 | null | transformers | 32,698 | Entry not found |
erickfm/t5-small-finetuned-bias-sweep-e5d0bea8 | 397d124e8d31923ec1c478538485357ad99a5e71 | 2022-06-07T20:17:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/t5-small-finetuned-bias-sweep-e5d0bea8 | 1 | null | transformers | 32,699 | Entry not found |
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