modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
minoosh/finetuned_roberta-base-on-IEMOCAP_3 | 2023-10-06T15:23:59.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | minoosh | null | null | minoosh/finetuned_roberta-base-on-IEMOCAP_3 | 0 | 2 | transformers | 2023-10-06T15:11:03 | ---
base_model: cardiffnlp/twitter-roberta-base-emotion
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_roberta-base-on-IEMOCAP_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_roberta-base-on-IEMOCAP_3
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0811
- Accuracy: 0.6481
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8961 | 1.0 | 110 | 0.8901 | 0.6435 |
| 0.7964 | 2.0 | 220 | 0.8135 | 0.6731 |
| 0.6132 | 3.0 | 330 | 0.8108 | 0.6993 |
| 0.3977 | 4.0 | 440 | 0.8360 | 0.7198 |
| 0.308 | 5.0 | 550 | 0.8994 | 0.6982 |
| 0.264 | 6.0 | 660 | 1.0231 | 0.6970 |
| 0.2477 | 7.0 | 770 | 1.0716 | 0.6993 |
| 0.1822 | 8.0 | 880 | 1.0890 | 0.6970 |
| 0.1438 | 9.0 | 990 | 1.1732 | 0.7039 |
| 0.1445 | 10.0 | 1100 | 1.2182 | 0.6902 |
| 0.1143 | 11.0 | 1210 | 1.2504 | 0.6993 |
| 0.1266 | 12.0 | 1320 | 1.3046 | 0.6970 |
| 0.094 | 13.0 | 1430 | 1.3148 | 0.7130 |
| 0.0985 | 14.0 | 1540 | 1.3253 | 0.7175 |
| 0.0947 | 15.0 | 1650 | 1.3411 | 0.7118 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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minoosh/finetuned_roberta-base-on-IEMOCAP_5 | 2023-10-06T15:40:40.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | minoosh | null | null | minoosh/finetuned_roberta-base-on-IEMOCAP_5 | 0 | 2 | transformers | 2023-10-06T15:27:12 | ---
base_model: cardiffnlp/twitter-roberta-base-emotion
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned_roberta-base-on-IEMOCAP_5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_roberta-base-on-IEMOCAP_5
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5796
- Accuracy: 0.6833
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9167 | 1.0 | 108 | 0.9034 | 0.6477 |
| 0.7123 | 2.0 | 216 | 0.8683 | 0.6860 |
| 0.5267 | 3.0 | 324 | 0.8770 | 0.6953 |
| 0.387 | 4.0 | 432 | 0.9405 | 0.6965 |
| 0.3795 | 5.0 | 540 | 1.0109 | 0.7070 |
| 0.2305 | 6.0 | 648 | 1.0698 | 0.6977 |
| 0.1937 | 7.0 | 756 | 1.1674 | 0.7093 |
| 0.1661 | 8.0 | 864 | 1.2279 | 0.6907 |
| 0.2069 | 9.0 | 972 | 1.2690 | 0.7105 |
| 0.1453 | 10.0 | 1080 | 1.3482 | 0.6977 |
| 0.1251 | 11.0 | 1188 | 1.3402 | 0.7105 |
| 0.1433 | 12.0 | 1296 | 1.3726 | 0.7023 |
| 0.1243 | 13.0 | 1404 | 1.4308 | 0.7012 |
| 0.1295 | 14.0 | 1512 | 1.4511 | 0.7116 |
| 0.1235 | 15.0 | 1620 | 1.4420 | 0.7070 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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mjsp/india-sweet | 2023-10-22T14:04:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"image-classification",
"huggingpics",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | mjsp | null | null | mjsp/india-sweet | 0 | 2 | transformers | 2023-10-06T15:42:03 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: india-sweet
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# india-sweet
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### gulab jamun
#### jalebi
#### rasgulla
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fehimeyigit/job-ds-sample | 2023-10-06T16:05:25.000Z | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | fehimeyigit | null | null | fehimeyigit/job-ds-sample | 0 | 2 | transformers | 2023-10-06T15:42:53 | ---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: job-ds-sample
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. -->
# job-ds-sample
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 9.3241
- Validation Loss: 9.3151
- Epoch: 2
## 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:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -926, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.1445 | 9.9870 | 0 |
| 9.7568 | 9.6536 | 1 |
| 9.3241 | 9.3151 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
| 2,041 | [
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ekolasky/longformer_result_detection | 2023-10-17T00:09:51.000Z | [
"transformers",
"pytorch",
"longformer",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | ekolasky | null | null | ekolasky/longformer_result_detection | 0 | 2 | transformers | 2023-10-06T17:13:30 | ---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: longformer_result_detection
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. -->
# longformer_result_detection
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1366
- Precision: 0.6230
- Recall: 0.6881
- F1: 0.6539
- Accuracy: 0.9738
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 410 | 0.0770 | 0.5744 | 0.6056 | 0.5896 | 0.9720 |
| 0.1137 | 2.0 | 820 | 0.0612 | 0.5608 | 0.6398 | 0.5977 | 0.9723 |
| 0.056 | 3.0 | 1230 | 0.0671 | 0.6374 | 0.6861 | 0.6609 | 0.9753 |
| 0.0347 | 4.0 | 1640 | 0.0854 | 0.6026 | 0.7505 | 0.6685 | 0.9754 |
| 0.0221 | 5.0 | 2050 | 0.1062 | 0.6138 | 0.6781 | 0.6444 | 0.9732 |
| 0.0221 | 6.0 | 2460 | 0.1116 | 0.6181 | 0.7002 | 0.6566 | 0.9750 |
| 0.0119 | 7.0 | 2870 | 0.1143 | 0.6405 | 0.7565 | 0.6937 | 0.9768 |
| 0.007 | 8.0 | 3280 | 0.1303 | 0.6712 | 0.6901 | 0.6806 | 0.9747 |
| 0.0058 | 9.0 | 3690 | 0.1347 | 0.6189 | 0.6861 | 0.6508 | 0.9741 |
| 0.0036 | 10.0 | 4100 | 0.1366 | 0.6230 | 0.6881 | 0.6539 | 0.9738 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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adityarra07/whisper-medium-ft-czech-data-2 | 2023-10-06T19:27:16.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-czech-data-2 | 0 | 2 | transformers | 2023-10-06T17:59:06 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-czech-data-2
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. -->
# whisper-medium-ft-czech-data-2
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2582
- Wer: 14.0537
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3954 | 1.0 | 789 | 0.2582 | 14.0537 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
naitik7/terrain-detection-demo | 2023-10-06T19:04:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | naitik7 | null | null | naitik7/terrain-detection-demo | 0 | 2 | transformers | 2023-10-06T19:03:53 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: terrain-detection-demo
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9111111164093018
---
# terrain-detection-demo
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### grassy
#### marshy
#### rocky
#### sandy
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LoneStriker/CollectiveCognition-v1.1-Mistral-7B-3.0bpw-h6-exl2 | 2023-10-06T20:20:00.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"mistral-7b",
"instruct",
"finetune",
"gpt4",
"synthetic data",
"distillation",
"sharegpt",
"en",
"dataset:CollectiveCognition/chats-data-2023-09-27",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/CollectiveCognition-v1.1-Mistral-7B-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-06T20:14:10 | ---
base_model: NousResearch/Llama-2-13b-hf
tags:
- mistral-7b
- instruct
- finetune
- gpt4
- synthetic data
- distillation
- sharegpt
datasets:
- CollectiveCognition/chats-data-2023-09-27
model-index:
- name: CollectiveCognition-v1-Mistral-7B
results: []
license: apache-2.0
language:
- en
---
**Collective Cognition v1.1 - Mistral 7B**
<div style="display: flex; justify-content: center;">
<a href="https://collectivecognition.ai" target="_blank" style="display: inline-block; text-align: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/DNZXsJE5oC_rM8eYY6H_x.png" alt="Collective Cognition Logo" width="50%" style="display: block; margin: 0 auto;">
</a>
</div>
## Model Description:
Collective Cognition v1.1 is a state-of-the-art model fine-tuned using the Mistral approach. This model is particularly notable for its performance, outperforming many 70B models on the TruthfulQA benchmark. This benchmark assesses models for common misconceptions, potentially indicating hallucination rates.
## Special Features:
- **Quick Training**: This model was trained in just 3 minutes on a single 4090 with a qlora, and competes with 70B scale Llama-2 Models at TruthfulQA.
- **Limited Data**: Despite its exceptional performance, it was trained on only ONE HUNDRED data points, all of which were gathered from a platform reminiscent of ShareGPT.
- **Extreme TruthfulQA Benchmark**: This model is competing strongly with top 70B models on the TruthfulQA benchmark despite the small dataset and qlora training!
## Acknowledgements:
Special thanks to @a16z and all contributors to the Collective Cognition dataset for making the development of this model possible.
## Dataset:
The model was trained using data from the Collective Cognition website. The efficacy of this dataset is demonstrated by the model's stellar performance, suggesting that further expansion of this dataset could yield even more promising results. The data is reminiscent of that collected from platforms like ShareGPT.
You can contribute to the growth of the dataset by sharing your own ChatGPT chats [here](https://CollectiveCognition.ai).
You can download the datasets created by Collective Cognition here: https://huggingface.co/CollectiveCognition
## Performance:
- **TruthfulQA**: Collective Cognition v1.1 has notably outperformed various 70B models on the TruthfulQA benchmark, highlighting its ability to understand and rectify common misconceptions.
## Usage:
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
OR
```
<system message>
USER: <prompt>
ASSISTANT:
```
## Benchmarks:
Collective Cognition v1.0 TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4051|± |0.0172|
| | |mc2 |0.5738|± |0.0157|
```
Collective Cognition v1.1 GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5085|± |0.0146|
| | |acc_norm|0.5384|± |0.0146|
|arc_easy | 0|acc |0.7963|± |0.0083|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8495|± |0.0063|
|hellaswag | 0|acc |0.6399|± |0.0048|
| | |acc_norm|0.8247|± |0.0038|
|openbookqa | 0|acc |0.3240|± |0.0210|
| | |acc_norm|0.4540|± |0.0223|
|piqa | 0|acc |0.7992|± |0.0093|
| | |acc_norm|0.8107|± |0.0091|
|winogrande | 0|acc |0.7348|± |0.0124|
Average: 71.13
```
AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.1929|± |0.0248|
| | |acc_norm|0.2008|± |0.0252|
|agieval_logiqa_en | 0|acc |0.3134|± |0.0182|
| | |acc_norm|0.3333|± |0.0185|
|agieval_lsat_ar | 0|acc |0.2217|± |0.0275|
| | |acc_norm|0.2043|± |0.0266|
|agieval_lsat_lr | 0|acc |0.3412|± |0.0210|
| | |acc_norm|0.3216|± |0.0207|
|agieval_lsat_rc | 0|acc |0.4721|± |0.0305|
| | |acc_norm|0.4201|± |0.0301|
|agieval_sat_en | 0|acc |0.6068|± |0.0341|
| | |acc_norm|0.5777|± |0.0345|
|agieval_sat_en_without_passage| 0|acc |0.3932|± |0.0341|
| | |acc_norm|0.3641|± |0.0336|
|agieval_sat_math | 0|acc |0.2864|± |0.0305|
| | |acc_norm|0.2636|± |0.0298|
Average: 33.57
```
Training run on wandb here: https://wandb.ai/teknium1/collectivecognition-mistral-7b/runs/collectivecognition-mistral-8/workspace
## Licensing:
Apache 2.0
---
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AustinMcMike/Stevie_v10 | 2023-10-07T04:31:57.000Z | [
"peft",
"region:us"
] | null | AustinMcMike | null | null | AustinMcMike/Stevie_v10 | 0 | 2 | peft | 2023-10-07T04:31:45 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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SakshiRathi77/wav2vec2-in-hindi | 2023-10-10T17:31:45.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | automatic-speech-recognition | SakshiRathi77 | null | null | SakshiRathi77/wav2vec2-in-hindi | 0 | 2 | transformers | 2023-10-07T18:35:38 | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-in-hindi
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-in-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6122
- Wer: 1.0343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.586 | 3.17 | 100 | 3.4128 | 1.0 |
| 3.4004 | 6.35 | 200 | 3.3716 | 1.0 |
| 3.3097 | 9.52 | 300 | 3.0612 | 1.0 |
| 1.903 | 12.7 | 400 | 0.8674 | 1.0476 |
| 0.636 | 15.87 | 500 | 0.6154 | 1.0383 |
| 0.3542 | 19.05 | 600 | 0.6023 | 1.0283 |
| 0.2611 | 22.22 | 700 | 0.6086 | 1.0283 |
| 0.2123 | 25.4 | 800 | 0.6135 | 1.0296 |
| 0.1729 | 28.57 | 900 | 0.6221 | 1.0231 |
| 0.1543 | 31.75 | 1000 | 0.6063 | 1.0356 |
| 0.1386 | 34.92 | 1100 | 0.6090 | 1.0258 |
| 0.1248 | 38.1 | 1200 | 0.6122 | 1.0343 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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luxmorocco/qiyas-falcon-7b | 2023-10-07T20:00:35.000Z | [
"peft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | luxmorocco | null | null | luxmorocco/qiyas-falcon-7b | 0 | 2 | peft | 2023-10-07T19:45:57 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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#### Preprocessing [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
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] |
DopeorNope/ZeroCoke3_kor_adapter | 2023-10-07T19:52:19.000Z | [
"peft",
"region:us"
] | null | DopeorNope | null | null | DopeorNope/ZeroCoke3_kor_adapter | 0 | 2 | peft | 2023-10-07T19:50:19 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
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Buseak/spellcorrector_710_v8 | 2023-10-07T23:42:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Buseak | null | null | Buseak/spellcorrector_710_v8 | 0 | 2 | transformers | 2023-10-07T20:12:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: spellcorrector_710_v8
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. -->
# spellcorrector_710_v8
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0144
- Precision: 0.9961
- Recall: 0.9962
- F1: 0.9962
- Accuracy: 0.9957
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2237 | 1.0 | 1951 | 0.1844 | 0.9061 | 0.9700 | 0.9370 | 0.9557 |
| 0.1882 | 2.0 | 3902 | 0.1577 | 0.9144 | 0.9719 | 0.9423 | 0.9598 |
| 0.1669 | 3.0 | 5853 | 0.1389 | 0.9311 | 0.9689 | 0.9497 | 0.9633 |
| 0.154 | 4.0 | 7804 | 0.1234 | 0.9343 | 0.9751 | 0.9543 | 0.9669 |
| 0.141 | 5.0 | 9755 | 0.1076 | 0.9480 | 0.9734 | 0.9605 | 0.9711 |
| 0.1286 | 6.0 | 11706 | 0.0959 | 0.9584 | 0.9746 | 0.9664 | 0.9747 |
| 0.1131 | 7.0 | 13657 | 0.0799 | 0.9624 | 0.9792 | 0.9708 | 0.9780 |
| 0.1016 | 8.0 | 15608 | 0.0714 | 0.9696 | 0.9801 | 0.9748 | 0.9805 |
| 0.0915 | 9.0 | 17559 | 0.0627 | 0.9737 | 0.9821 | 0.9779 | 0.9825 |
| 0.0839 | 10.0 | 19510 | 0.0574 | 0.9781 | 0.9830 | 0.9806 | 0.9839 |
| 0.0761 | 11.0 | 21461 | 0.0500 | 0.9808 | 0.9849 | 0.9828 | 0.9858 |
| 0.069 | 12.0 | 23412 | 0.0437 | 0.9807 | 0.9887 | 0.9847 | 0.9873 |
| 0.0644 | 13.0 | 25363 | 0.0404 | 0.9849 | 0.9882 | 0.9866 | 0.9882 |
| 0.057 | 14.0 | 27314 | 0.0371 | 0.9871 | 0.9892 | 0.9881 | 0.9892 |
| 0.0555 | 15.0 | 29265 | 0.0343 | 0.9890 | 0.9895 | 0.9893 | 0.9900 |
| 0.0512 | 16.0 | 31216 | 0.0288 | 0.9899 | 0.9919 | 0.9909 | 0.9914 |
| 0.0464 | 17.0 | 33167 | 0.0265 | 0.9914 | 0.9922 | 0.9918 | 0.9920 |
| 0.0424 | 18.0 | 35118 | 0.0234 | 0.9924 | 0.9937 | 0.9931 | 0.9929 |
| 0.0391 | 19.0 | 37069 | 0.0215 | 0.9940 | 0.9936 | 0.9938 | 0.9936 |
| 0.0375 | 20.0 | 39020 | 0.0195 | 0.9944 | 0.9944 | 0.9944 | 0.9942 |
| 0.0344 | 21.0 | 40971 | 0.0178 | 0.9952 | 0.9949 | 0.9950 | 0.9947 |
| 0.032 | 22.0 | 42922 | 0.0160 | 0.9955 | 0.9957 | 0.9956 | 0.9952 |
| 0.0299 | 23.0 | 44873 | 0.0153 | 0.9957 | 0.9960 | 0.9958 | 0.9954 |
| 0.0291 | 24.0 | 46824 | 0.0145 | 0.9961 | 0.9961 | 0.9961 | 0.9957 |
| 0.0289 | 25.0 | 48775 | 0.0144 | 0.9961 | 0.9962 | 0.9962 | 0.9957 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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] |
hongyin/informer-0.2b-4k | 2023-10-22T10:20:02.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | hongyin | null | null | hongyin/informer-0.2b-4k | 1 | 2 | transformers | 2023-10-08T01:20:18 | ---
language:
- en
- zh
pipeline_tag: text-generation
---
## hongyin/informer-0.2b
I am pleased to introduce to you an English-Chinese bilingual autoregressive language model. This model is trained from scratch and has a unique vocabulary and 20 million parameters based on the LLAMA2 model structure. Our goal is to provide a solution that is computationally cheap and easy to inference. It's important to note that this is a base model, not intended to be used as a chatbot, but rather for alchemy. We look forward to providing you with a practical model product.
To put aside the damn high-sounding words, the name of each model has rich connotations and personal experience, including the previous model, and it is worth reminding us repeatedly.
```python
```
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
--- | 1,110 | [
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checkiejan/multi-qa-mpnet-base-dot-v1-covidqa-search-75-25-1epoch-full | 2023-10-08T01:32:16.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | checkiejan | null | null | checkiejan/multi-qa-mpnet-base-dot-v1-covidqa-search-75-25-1epoch-full | 0 | 2 | sentence-transformers | 2023-10-08T01:31:30 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2455 with parameters:
```
{'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 245,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,496 | [
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alex-tecky/whisper-tiny-zh-HK-youtube | 2023-10-08T05:28:36.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yue",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | alex-tecky | null | null | alex-tecky/whisper-tiny-zh-HK-youtube | 0 | 2 | transformers | 2023-10-08T05:06:35 | ---
language:
- yue
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper tiny - Cantonese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
metrics:
- name: Wer
type: wer
value: 91.1543287327478
---
<!-- 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. -->
# Whisper tiny - Cantonese
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6331
- Wer Ortho: 89.4250
- Wer: 91.1543
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.6724 | 0.57 | 500 | 0.6331 | 89.4250 | 91.1543 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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m-aliabbas1/mva_ner | 2023-10-08T06:02:04.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | m-aliabbas1 | null | null | m-aliabbas1/mva_ner | 0 | 2 | transformers | 2023-10-08T06:02:03 | ---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: mva_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. -->
# mva_ner
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0
- Year F1: 1.0
- Years Ago F1: 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Year F1 | Years Ago F1 |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------:|:------------:|
| 0.0171 | 55.56 | 1000 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 111.11 | 2000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
UNIST-Eunchan/pegasus-x-base-arxiv | 2023-10-13T05:09:53.000Z | [
"transformers",
"pytorch",
"pegasus_x",
"text2text-generation",
"generated_from_trainer",
"dataset:arxiv-summarization",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | UNIST-Eunchan | null | null | UNIST-Eunchan/pegasus-x-base-arxiv | 0 | 2 | transformers | 2023-10-08T06:19:57 | ---
base_model: google/pegasus-x-base
tags:
- generated_from_trainer
datasets:
- arxiv-summarization
model-index:
- name: pegasus-x-base-arxiv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-x-base-arxiv
This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/google/pegasus-x-base) on the arxiv-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:------:|:---------------:|
| 33079752.704 | 0.02 | 1000 | 20.8725 |
| 137412599.808 | 0.04 | 2000 | 20.8725 |
| 4579383.808 | 0.06 | 3000 | 20.8725 |
| 7668121.088 | 0.08 | 4000 | 20.8725 |
| 2206938.88 | 0.1 | 5000 | 20.8725 |
| 733122.432 | 0.12 | 6000 | 20.8725 |
| 91004.528 | 0.14 | 7000 | 20.8725 |
| 180892.96 | 0.16 | 8000 | 20.8725 |
| 55383486.464 | 0.18 | 9000 | 20.8725 |
| 3599870.464 | 0.2 | 10000 | 20.8725 |
| 2522732.288 | 0.22 | 11000 | 20.8725 |
| 27543791.616 | 0.24 | 12000 | 20.8725 |
| 55559.692 | 0.26 | 13000 | 20.8725 |
| 30380011.52 | 0.28 | 14000 | 20.8725 |
| 6128657.92 | 0.3 | 15000 | 20.8725 |
| 1285824.128 | 0.32 | 16000 | 20.8725 |
| 50138685.44 | 0.33 | 17000 | 20.8725 |
| 1199289.088 | 0.35 | 18000 | 20.8725 |
| 439883.712 | 0.37 | 19000 | 20.8725 |
| 833669562.368 | 0.39 | 20000 | 20.8725 |
| 3537328.896 | 0.41 | 21000 | 20.8725 |
| 602138869.76 | 0.43 | 22000 | 20.8725 |
| 32460343.296 | 0.45 | 23000 | 20.8725 |
| 438640345.088 | 0.47 | 24000 | 20.8725 |
| 600373.568 | 0.49 | 25000 | 20.8725 |
| 504883.84 | 0.51 | 26000 | 20.8725 |
| 2854624.0 | 0.53 | 27000 | 20.8725 |
| 3245419.264 | 0.55 | 28000 | 20.8725 |
| 5536696.32 | 0.57 | 29000 | 20.8725 |
| 157420208.128 | 0.59 | 30000 | 20.8725 |
| 6593876.992 | 0.61 | 31000 | 20.8725 |
| 4113536.512 | 0.63 | 32000 | 20.8725 |
| 425032777.728 | 0.65 | 33000 | 20.8725 |
| 1103064.704 | 0.67 | 34000 | 20.8725 |
| 3796103.424 | 0.69 | 35000 | 20.8725 |
| 709982.272 | 0.71 | 36000 | 20.8725 |
| 635942.592 | 0.73 | 37000 | 20.8725 |
| 12475876.352 | 0.75 | 38000 | 20.8725 |
| 977161.344 | 0.77 | 39000 | 20.8725 |
| 215117201.408 | 0.79 | 40000 | 20.8725 |
| 2228061.184 | 0.81 | 41000 | 20.8725 |
| 1511801.472 | 0.83 | 42000 | 20.8725 |
| 374335.392 | 0.85 | 43000 | 20.8725 |
| 551274414.08 | 0.87 | 44000 | 20.8725 |
| 14776.275 | 0.89 | 45000 | 20.8725 |
| 1016692.48 | 0.91 | 46000 | 20.8725 |
| 1531294.592 | 0.93 | 47000 | 20.8725 |
| 14540307.456 | 0.95 | 48000 | 20.8725 |
| 187237.904 | 0.97 | 49000 | 20.8725 |
| 1191666.432 | 0.99 | 50000 | 20.8725 |
| 37780086.784 | 1.0 | 51000 | 20.8725 |
| 278421.152 | 1.02 | 52000 | 20.8725 |
| 32245749.76 | 1.04 | 53000 | 20.8725 |
| 8463674.368 | 1.06 | 54000 | 20.8725 |
| 12945154.048 | 1.08 | 55000 | 20.8725 |
| 43897995.264 | 1.1 | 56000 | 20.8725 |
| 1705616.384 | 1.12 | 57000 | 20.8725 |
| 14537140.224 | 1.14 | 58000 | 20.8725 |
| 2372431.104 | 1.16 | 59000 | 20.8725 |
| 13542864.896 | 1.18 | 60000 | 20.8725 |
| 622085079.04 | 1.2 | 61000 | 20.8725 |
| 41270423.552 | 1.22 | 62000 | 20.8725 |
| 676841.664 | 1.24 | 63000 | 20.8725 |
| 186303217.664 | 1.26 | 64000 | 20.8725 |
| 89201172.48 | 1.28 | 65000 | 20.8725 |
| 197829165.056 | 1.3 | 66000 | 20.8725 |
| 7490446.336 | 1.32 | 67000 | 20.8725 |
| 44490223.616 | 1.34 | 68000 | nan |
| 0.0 | 1.36 | 69000 | nan |
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| 0.0 | 1.4 | 71000 | nan |
| 0.0 | 1.42 | 72000 | nan |
| 0.0 | 1.44 | 73000 | nan |
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| 0.0 | 1.5 | 76000 | nan |
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| 0.0 | 1.56 | 79000 | nan |
| 0.0 | 1.58 | 80000 | nan |
| 0.0 | 1.6 | 81000 | nan |
| 0.0 | 1.62 | 82000 | nan |
| 0.0 | 1.64 | 83000 | nan |
| 0.0 | 1.65 | 84000 | nan |
| 0.0 | 1.67 | 85000 | nan |
| 0.0 | 1.69 | 86000 | nan |
| 0.0 | 1.71 | 87000 | nan |
| 0.0 | 1.73 | 88000 | nan |
| 0.0 | 1.75 | 89000 | nan |
| 0.0 | 1.77 | 90000 | nan |
| 0.0 | 1.79 | 91000 | nan |
| 0.0 | 1.81 | 92000 | nan |
| 0.0 | 1.83 | 93000 | nan |
| 0.0 | 1.85 | 94000 | nan |
| 0.0 | 1.87 | 95000 | nan |
| 0.0 | 1.89 | 96000 | nan |
| 0.0 | 1.91 | 97000 | nan |
| 0.0 | 1.93 | 98000 | nan |
| 0.0 | 1.95 | 99000 | nan |
| 0.0 | 1.97 | 100000 | nan |
| 0.0 | 1.99 | 101000 | nan |
| 0.0 | 2.01 | 102000 | nan |
| 0.0 | 2.03 | 103000 | nan |
| 0.0 | 2.05 | 104000 | nan |
| 0.0 | 2.07 | 105000 | nan |
| 0.0 | 2.09 | 106000 | nan |
| 0.0 | 2.11 | 107000 | nan |
| 0.0 | 2.13 | 108000 | nan |
| 0.0 | 2.15 | 109000 | nan |
| 0.0 | 2.17 | 110000 | nan |
| 0.0 | 2.19 | 111000 | nan |
| 0.0 | 2.21 | 112000 | nan |
| 0.0 | 2.23 | 113000 | nan |
| 0.0 | 2.25 | 114000 | nan |
| 0.0 | 2.27 | 115000 | nan |
| 0.0 | 2.29 | 116000 | nan |
| 0.0 | 2.3 | 117000 | nan |
| 0.0 | 2.32 | 118000 | nan |
| 0.0 | 2.34 | 119000 | nan |
| 0.0 | 2.36 | 120000 | nan |
| 0.0 | 2.38 | 121000 | nan |
| 0.0 | 2.4 | 122000 | nan |
| 0.0 | 2.42 | 123000 | nan |
| 0.0 | 2.44 | 124000 | nan |
| 0.0 | 2.46 | 125000 | nan |
| 0.0 | 2.48 | 126000 | nan |
| 0.0 | 2.5 | 127000 | nan |
| 0.0 | 2.52 | 128000 | nan |
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| 0.0 | 2.6 | 132000 | nan |
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| 0.0 | 2.64 | 134000 | nan |
| 0.0 | 2.66 | 135000 | nan |
| 0.0 | 2.68 | 136000 | nan |
| 0.0 | 2.7 | 137000 | nan |
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| 0.0 | 2.82 | 143000 | nan |
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| 0.0 | 2.96 | 150000 | nan |
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| 0.0 | 2.99 | 152000 | nan |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
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] |
xszhou/test-sentiment-analysis | 2023-10-08T06:23:24.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | xszhou | null | null | xszhou/test-sentiment-analysis | 0 | 2 | transformers | 2023-10-08T06:23:12 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: test-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93192
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3067
- Accuracy: 0.9319
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1341 | 1.0 | 1563 | 0.2795 | 0.9236 |
| 0.0664 | 2.0 | 3126 | 0.3067 | 0.9319 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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sebastiantrbl/DialoGPT-dailydialog-6 | 2023-10-08T08:12:00.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:daily_dialog",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | sebastiantrbl | null | null | sebastiantrbl/DialoGPT-dailydialog-6 | 0 | 2 | transformers | 2023-10-08T07:39:30 | ---
license: mit
base_model: microsoft/DialoGPT-medium
tags:
- generated_from_trainer
datasets:
- daily_dialog
model-index:
- name: DialoGPT-dailydialog-6
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. -->
# DialoGPT-dailydialog-6
This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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GGital/CAI-Test-03 | 2023-10-08T08:36:53.000Z | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"region:us"
] | null | GGital | null | null | GGital/CAI-Test-03 | 0 | 2 | peft | 2023-10-08T08:36:35 | ---
library_name: peft
base_model: openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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] |
MohamedAE/distilbert-base-uncased-finetuned-emotion | 2023-10-08T16:05:32.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | MohamedAE | null | null | MohamedAE/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-08T10:41:38 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2165
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8315 | 1.0 | 250 | 0.3177 |
| 0.2488 | 2.0 | 500 | 0.2165 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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GGital/Fine_tune_03 | 2023-10-08T11:01:11.000Z | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"region:us"
] | null | GGital | null | null | GGital/Fine_tune_03 | 0 | 2 | peft | 2023-10-08T11:00:35 | ---
library_name: peft
base_model: openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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SaloniJhalani/ft-falcon-7b-instruct-adapters | 2023-10-08T11:15:45.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | SaloniJhalani | null | null | SaloniJhalani/ft-falcon-7b-instruct-adapters | 0 | 2 | peft | 2023-10-08T11:15:39 | ---
library_name: peft
base_model: tiiuae/falcon-7b-instruct
---
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## Training procedure
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- PEFT 0.6.0.dev0
## Training procedure
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RogerB/afro-xlmr-large-kin-sent2 | 2023-10-08T14:35:34.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | RogerB | null | null | RogerB/afro-xlmr-large-kin-sent2 | 0 | 2 | transformers | 2023-10-08T14:08:30 | ---
license: mit
base_model: Davlan/afro-xlmr-large
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: afro-xlmr-large-kin-sent2
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. -->
# afro-xlmr-large-kin-sent2
This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8396
- F1: 0.6881
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1000000
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9765 | 1.0 | 1013 | 0.7705 | 0.6683 |
| 0.7987 | 2.0 | 2026 | 0.6664 | 0.7397 |
| 0.7028 | 3.0 | 3039 | 0.5894 | 0.7720 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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whalesdotxyz/roberta-finetuned-subjqa-electronics | 2023-10-09T07:13:04.000Z | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | whalesdotxyz | null | null | whalesdotxyz/roberta-finetuned-subjqa-electronics | 0 | 2 | transformers | 2023-10-08T18:58:52 | ---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-electronics
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. -->
# roberta-finetuned-subjqa-electronics
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU | 2023-10-08T22:55:34.000Z | [
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | quastrinos | null | null | quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU | 0 | 2 | transformers | 2023-10-08T19:23:39 | ---
license: mit
base_model: quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2
tags:
- generated_from_keras_callback
model-index:
- name: race-openbook-finetuned-deberta-v3-large-mcqa-TPU
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. -->
# race-openbook-finetuned-deberta-v3-large-mcqa-TPU
This model is a fine-tuned version of [quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2](https://huggingface.co/quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7836
- Validation Loss: 1.0023
- Train Map3: 0.7779
- Train Lr: 5.0733553e-11
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 0.7836 | 1.0023 | 0.7779 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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hobbang/test | 2023-10-08T20:35:33.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | hobbang | null | null | hobbang/test | 0 | 2 | peft | 2023-10-08T20:35:24 | ---
library_name: peft
base_model: decapoda-research/llama-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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### Results
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1 | 2023-10-09T01:50:12.000Z | [
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | quastrinos | null | null | quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1 | 0 | 2 | transformers | 2023-10-08T20:50:27 | ---
license: mit
base_model: quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU
tags:
- generated_from_keras_callback
model-index:
- name: race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1
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. -->
# race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1
This model is a fine-tuned version of [quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU](https://huggingface.co/quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7361
- Validation Loss: 0.9878
- Train Map3: 0.7749
- Train Lr: 5.0733553e-11
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 0.7361 | 0.9878 | 0.7749 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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erikaxenia/donut-base-sroie-2 | 2023-10-08T22:58:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | erikaxenia | null | null | erikaxenia/donut-base-sroie-2 | 0 | 2 | transformers | 2023-10-08T22:05:54 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: donut-base-sroie-2
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. -->
# donut-base-sroie-2
This model is a fine-tuned version of [erikaxenia/donut-base-sroie-2](https://huggingface.co/erikaxenia/donut-base-sroie-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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yentinglin/Taiwan-LLM-7B-v2.1-base | 2023-11-02T08:44:45.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | yentinglin | null | null | yentinglin/Taiwan-LLM-7B-v2.1-base | 0 | 2 | transformers | 2023-10-09T03:09:20 |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: apache-2.0
language:
- zh
widget:
- text: >-
A chat between a curious user and an artificial intelligence assistant.
The assistant gives helpful, detailed, and polite answers to the user's
questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Acknowledge license to accept the repository.
extra_gated_prompt: Please contact the author for access.
extra_gated_button_content: Acknowledge license 同意以上內容
extra_gated_fields:
Name: text
Mail: text
Organization: text
Country: text
Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox
使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox
---
# 🌟 Checkout New [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
# Taiwan LLM based on Mistral-7B-v0.1
continue pretraining on 20 billion tokens in traditional Mandarin and instruction fine-tuning on millions of conversations.
This version does NOT include CommonCrawl.
# Collaboration with Ubitus K.K. 💪💪💪
本項目與 Ubitus K.K. 合作進行。Ubitus 為本項目提供寶貴的技術支持和計算資源。
Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable technical support and compute resources for the project.
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rmuema/kaggle-x-elo-finetune-v1.2 | 2023-10-09T03:14:03.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | rmuema | null | null | rmuema/kaggle-x-elo-finetune-v1.2 | 0 | 2 | peft | 2023-10-09T03:13:55 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
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gbarone77/llama-2-7b-camoscio10p-adapter | 2023-10-09T08:52:59.000Z | [
"peft",
"region:us"
] | null | gbarone77 | null | null | gbarone77/llama-2-7b-camoscio10p-adapter | 0 | 2 | peft | 2023-10-09T07:58:24 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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osmanh/data_science_inertview_QA | 2023-10-09T09:55:41.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | osmanh | null | null | osmanh/data_science_inertview_QA | 0 | 2 | peft | 2023-10-09T09:21:56 | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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ravikumar1478/Sentiment_analysis_on_Telugu_movie_Review_3 | 2023-10-09T09:36:37.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | ravikumar1478 | null | null | ravikumar1478/Sentiment_analysis_on_Telugu_movie_Review_3 | 0 | 2 | transformers | 2023-10-09T09:35:57 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Telugu_sentiment_movie
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. -->
# Telugu_sentiment_movie
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 4
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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RogerB/kinyaRoberta-large-kinte-finetuned-kin-sent2 | 2023-10-09T09:55:40.000Z | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | RogerB | null | null | RogerB/kinyaRoberta-large-kinte-finetuned-kin-sent2 | 0 | 2 | transformers | 2023-10-09T09:49:54 | ---
base_model: RogerB/kinyaRoberta-large-kinte-finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: kinyaRoberta-large-kinte-finetuned-kin-sent2
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. -->
# kinyaRoberta-large-kinte-finetuned-kin-sent2
This model is a fine-tuned version of [RogerB/kinyaRoberta-large-kinte-finetuned](https://huggingface.co/RogerB/kinyaRoberta-large-kinte-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8884
- F1: 0.6618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1000000
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9182 | 1.0 | 1013 | 0.6955 | 0.7017 |
| 0.7183 | 2.0 | 2026 | 0.5727 | 0.7785 |
| 0.6221 | 3.0 | 3039 | 0.5446 | 0.7958 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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shubhamgantayat/gpt2-wet-strength-model | 2023-10-09T11:17:52.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | shubhamgantayat | null | null | shubhamgantayat/gpt2-wet-strength-model | 0 | 2 | transformers | 2023-10-09T11:12:27 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-wet-strength-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wet-strength-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Joetib/results | 2023-10-09T12:01:59.000Z | [
"transformers",
"mistral",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Joetib | null | null | Joetib/results | 0 | 2 | transformers | 2023-10-09T11:59:54 | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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caoldr/whisper-medium-da | 2023-10-09T13:11:36.000Z | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"whisper-event",
"da",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | caoldr | null | null | caoldr/whisper-medium-da | 0 | 2 | transformers | 2023-10-09T12:00:35 | ---
language:
- da
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Danish (CV11 + FLEAURS)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: da
split: test
metrics:
- name: Wer
type: wer
value: 13.708574434508153
---
<!-- 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. -->
# Whisper Medium Danish (CV11 + FLEAURS)
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs da,da_dk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5814
- Wer: 13.7086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 32
- 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: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0265 | 3.14 | 1000 | 0.3690 | 14.7607 |
| 0.0063 | 6.29 | 2000 | 0.4342 | 14.0926 |
| 0.0016 | 9.43 | 3000 | 0.4847 | 14.3609 |
| 0.002 | 12.58 | 4000 | 0.4919 | 14.1715 |
| 0.0013 | 15.72 | 5000 | 0.5114 | 14.2294 |
| 0.0014 | 18.87 | 6000 | 0.5197 | 13.9137 |
| 0.0003 | 22.01 | 7000 | 0.5422 | 14.1978 |
| 0.0001 | 25.16 | 8000 | 0.5659 | 13.8716 |
| 0.0001 | 28.3 | 9000 | 0.5772 | 13.7296 |
| 0.0001 | 31.45 | 10000 | 0.5814 | 13.7086 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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RobinSchaefer/distilbert-base-uncased-finetuned_emotion | 2023-10-09T15:30:24.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | RobinSchaefer | null | null | RobinSchaefer/distilbert-base-uncased-finetuned_emotion | 0 | 2 | transformers | 2023-10-09T12:21:44 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned_emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8965
- name: F1
type: f1
value: 0.891778398424917
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned_emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3656
- Accuracy: 0.8965
- F1: 0.8918
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5894 | 0.796 | 0.7544 |
| 0.772 | 2.0 | 250 | 0.3656 | 0.8965 | 0.8918 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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whalesdotxyz/metacade_queston_answer_distlbert | 2023-10-09T13:33:54.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | whalesdotxyz | null | null | whalesdotxyz/metacade_queston_answer_distlbert | 0 | 2 | transformers | 2023-10-09T13:32:28 | ---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
model-index:
- name: metacade_queston_answer_distlbert
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. -->
# metacade_queston_answer_distlbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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GGital/Fine-Tune_Eng-01 | 2023-10-09T16:25:47.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | GGital | null | null | GGital/Fine-Tune_Eng-01 | 0 | 2 | peft | 2023-10-09T16:19:36 | ---
library_name: peft
base_model: hpcai-tech/Colossal-LLaMA-2-7b-base
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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#### Training Hyperparameters
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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AustinMcMike/Stevie_llama_v1 | 2023-10-09T18:46:38.000Z | [
"peft",
"region:us"
] | null | AustinMcMike | null | null | AustinMcMike/Stevie_llama_v1 | 0 | 2 | peft | 2023-10-09T18:36:28 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
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vedantjumle/indo-ml-final-test-xlnet-1 | 2023-10-09T21:15:24.000Z | [
"transformers",
"tf",
"xlnet",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | vedantjumle | null | null | vedantjumle/indo-ml-final-test-xlnet-1 | 0 | 2 | transformers | 2023-10-09T21:09:25 | ---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/indo-ml-final-test-xlnet-1
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. -->
# vedantjumle/indo-ml-final-test-xlnet-1
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7254
- Validation Loss: 0.6440
- Train Accuracy: 0.88
- Epoch: 9
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 5.1411 | 5.0487 | 0.01 | 0 |
| 5.0561 | 4.8924 | 0.0267 | 1 |
| 4.8362 | 4.4063 | 0.2033 | 2 |
| 4.2361 | 3.4961 | 0.45 | 3 |
| 3.3575 | 2.5506 | 0.6167 | 4 |
| 2.5070 | 1.8625 | 0.74 | 5 |
| 1.8507 | 1.3431 | 0.8067 | 6 |
| 1.3367 | 0.9980 | 0.84 | 7 |
| 0.9787 | 0.7747 | 0.86 | 8 |
| 0.7254 | 0.6440 | 0.88 | 9 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Sguzman4532/marian-finetuned-kde4-en-to-fr | 2023-10-10T18:30:43.000Z | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | Sguzman4532 | null | null | Sguzman4532/marian-finetuned-kde4-en-to-fr | 0 | 2 | transformers | 2023-10-10T00:50:21 | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: marian-finetuned-kde4-en-to-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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-10T01:42:40.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-10T01:36:34 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8689
- F1: 0.5385
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.7255 | 0.5652 |
| No log | 2.0 | 68 | 0.6728 | 0.0 |
| No log | 3.0 | 102 | 0.6611 | 0.0 |
| No log | 4.0 | 136 | 0.6646 | 0.5517 |
| No log | 5.0 | 170 | 0.7288 | 0.5294 |
| No log | 6.0 | 204 | 0.7022 | 0.4828 |
| No log | 7.0 | 238 | 0.6703 | 0.4545 |
| No log | 8.0 | 272 | 0.7291 | 0.5217 |
| No log | 9.0 | 306 | 0.9240 | 0.5517 |
| No log | 10.0 | 340 | 0.8689 | 0.5385 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-10T01:45:54.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-10T01:40:00 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4528
- F1: 0.8
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.6746 | 0.3333 |
| No log | 2.0 | 68 | 0.5840 | 0.2667 |
| No log | 3.0 | 102 | 0.4720 | 0.6 |
| No log | 4.0 | 136 | 0.4944 | 0.7857 |
| No log | 5.0 | 170 | 0.4389 | 0.8182 |
| No log | 6.0 | 204 | 0.4127 | 0.7857 |
| No log | 7.0 | 238 | 0.5503 | 0.5556 |
| No log | 8.0 | 272 | 0.3810 | 0.7692 |
| No log | 9.0 | 306 | 0.4450 | 0.8333 |
| No log | 10.0 | 340 | 0.4528 | 0.8 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-10T01:54:18.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-10T01:47:17 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0810
- F1: 0.5455
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.7090 | 0.5238 |
| No log | 2.0 | 68 | 0.6538 | 0.0 |
| No log | 3.0 | 102 | 0.6725 | 0.125 |
| No log | 4.0 | 136 | 0.6395 | 0.6667 |
| No log | 5.0 | 170 | 0.6769 | 0.4762 |
| No log | 6.0 | 204 | 0.6247 | 0.6923 |
| No log | 7.0 | 238 | 0.8995 | 0.3333 |
| No log | 8.0 | 272 | 0.9264 | 0.5217 |
| No log | 9.0 | 306 | 0.9714 | 0.5455 |
| No log | 10.0 | 340 | 1.0810 | 0.5455 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-10T01:57:39.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-10T01:51:52 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4476
- F1: 0.7200
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 1.0421 | 0.6875 |
| No log | 2.0 | 68 | 0.9179 | 0.5 |
| No log | 3.0 | 102 | 1.0639 | 0.7407 |
| No log | 4.0 | 136 | 1.4829 | 0.7143 |
| No log | 5.0 | 170 | 1.4051 | 0.7200 |
| No log | 6.0 | 204 | 1.2681 | 0.7500 |
| No log | 7.0 | 238 | 1.4626 | 0.7500 |
| No log | 8.0 | 272 | 1.4602 | 0.7200 |
| No log | 9.0 | 306 | 1.4207 | 0.7200 |
| No log | 10.0 | 340 | 1.4476 | 0.7200 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 2023-10-10T02:05:25.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 0 | 2 | transformers | 2023-10-10T01:59:01 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9367
- F1: 0.5833
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.9999 | 0.4211 |
| No log | 2.0 | 68 | 1.5323 | 0.2353 |
| No log | 3.0 | 102 | 2.1091 | 0.3333 |
| No log | 4.0 | 136 | 1.5779 | 0.5714 |
| No log | 5.0 | 170 | 1.6484 | 0.6087 |
| No log | 6.0 | 204 | 2.0530 | 0.6087 |
| No log | 7.0 | 238 | 1.8844 | 0.6364 |
| No log | 8.0 | 272 | 2.0102 | 0.5714 |
| No log | 9.0 | 306 | 1.8512 | 0.64 |
| No log | 10.0 | 340 | 1.9367 | 0.5833 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 2023-10-10T02:09:18.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 0 | 2 | transformers | 2023-10-10T02:03:34 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9728
- F1: 0.7333
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 1.3388 | 0.7200 |
| No log | 2.0 | 68 | 1.3202 | 0.7586 |
| No log | 3.0 | 102 | 2.1590 | 0.6875 |
| No log | 4.0 | 136 | 1.6732 | 0.7407 |
| No log | 5.0 | 170 | 2.0201 | 0.6897 |
| No log | 6.0 | 204 | 2.8922 | 0.6667 |
| No log | 7.0 | 238 | 1.8322 | 0.6 |
| No log | 8.0 | 272 | 2.1121 | 0.7097 |
| No log | 9.0 | 306 | 2.0340 | 0.7097 |
| No log | 10.0 | 340 | 1.9728 | 0.7333 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 2023-10-10T02:16:39.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 0 | 2 | transformers | 2023-10-10T02:10:01 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6534
- F1: 0.4800
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.1874 | 0.6667 |
| No log | 2.0 | 68 | 1.4313 | 0.6364 |
| No log | 3.0 | 102 | 1.7422 | 0.5385 |
| No log | 4.0 | 136 | 2.8321 | 0.6061 |
| No log | 5.0 | 170 | 2.7824 | 0.4 |
| No log | 6.0 | 204 | 3.4404 | 0.4167 |
| No log | 7.0 | 238 | 3.4469 | 0.4615 |
| No log | 8.0 | 272 | 3.5503 | 0.4615 |
| No log | 9.0 | 306 | 3.6295 | 0.4800 |
| No log | 10.0 | 340 | 3.6534 | 0.4800 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 2023-10-10T02:21:29.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 0 | 2 | transformers | 2023-10-10T02:15:33 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1243
- F1: 0.7407
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.3352 | 0.6857 |
| No log | 2.0 | 68 | 1.3955 | 0.7586 |
| No log | 3.0 | 102 | 1.7741 | 0.6207 |
| No log | 4.0 | 136 | 1.3873 | 0.64 |
| No log | 5.0 | 170 | 2.7335 | 0.625 |
| No log | 6.0 | 204 | 2.8991 | 0.6061 |
| No log | 7.0 | 238 | 1.7389 | 0.6087 |
| No log | 8.0 | 272 | 2.6851 | 0.6667 |
| No log | 9.0 | 306 | 2.0888 | 0.7407 |
| No log | 10.0 | 340 | 2.1243 | 0.7407 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-10T02:28:46.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-10T02:21:17 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8595
- F1: 0.5833
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.1069 | 0.3158 |
| No log | 2.0 | 68 | 2.4423 | 0.4 |
| No log | 3.0 | 102 | 2.9767 | 0.5600 |
| No log | 4.0 | 136 | 3.2254 | 0.5185 |
| No log | 5.0 | 170 | 3.4519 | 0.6000 |
| No log | 6.0 | 204 | 2.5190 | 0.64 |
| No log | 7.0 | 238 | 2.9576 | 0.6154 |
| No log | 8.0 | 272 | 3.0403 | 0.5926 |
| No log | 9.0 | 306 | 2.9991 | 0.5833 |
| No log | 10.0 | 340 | 2.8595 | 0.5833 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-10T02:33:40.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-10T02:27:42 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3761
- F1: 0.6667
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.7356 | 0.6486 |
| No log | 2.0 | 68 | 1.8839 | 0.6364 |
| No log | 3.0 | 102 | 1.6830 | 0.6 |
| No log | 4.0 | 136 | 2.3409 | 0.6429 |
| No log | 5.0 | 170 | 2.5296 | 0.6897 |
| No log | 6.0 | 204 | 2.1268 | 0.6923 |
| No log | 7.0 | 238 | 2.1711 | 0.7143 |
| No log | 8.0 | 272 | 2.4146 | 0.6897 |
| No log | 9.0 | 306 | 2.4613 | 0.6429 |
| No log | 10.0 | 340 | 2.3761 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 2023-10-10T02:41:02.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 0 | 2 | transformers | 2023-10-10T02:33:29 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9476
- F1: 0.5926
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.7878 | 0.5926 |
| No log | 2.0 | 68 | 2.7388 | 0.6154 |
| No log | 3.0 | 102 | 2.4709 | 0.6154 |
| No log | 4.0 | 136 | 2.9629 | 0.5517 |
| No log | 5.0 | 170 | 2.4677 | 0.6154 |
| No log | 6.0 | 204 | 3.0477 | 0.5926 |
| No log | 7.0 | 238 | 3.3382 | 0.5926 |
| No log | 8.0 | 272 | 2.6221 | 0.6154 |
| No log | 9.0 | 306 | 2.7802 | 0.6154 |
| No log | 10.0 | 340 | 2.9476 | 0.5926 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 2023-10-10T02:45:53.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 0 | 2 | transformers | 2023-10-10T02:40:07 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5942
- F1: 0.6154
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.6455 | 0.6857 |
| No log | 2.0 | 68 | 4.4882 | 0.6667 |
| No log | 3.0 | 102 | 1.8979 | 0.6429 |
| No log | 4.0 | 136 | 1.8151 | 0.6667 |
| No log | 5.0 | 170 | 2.5707 | 0.6429 |
| No log | 6.0 | 204 | 3.0281 | 0.6452 |
| No log | 7.0 | 238 | 3.0813 | 0.6667 |
| No log | 8.0 | 272 | 2.7164 | 0.6207 |
| No log | 9.0 | 306 | 2.6043 | 0.5833 |
| No log | 10.0 | 340 | 2.5942 | 0.6154 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 2023-10-10T02:52:54.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 0 | 2 | transformers | 2023-10-10T02:45:49 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9151
- F1: 0.5
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.1691 | 0.4 |
| No log | 2.0 | 68 | 2.3622 | 0.6154 |
| No log | 3.0 | 102 | 2.4377 | 0.5185 |
| No log | 4.0 | 136 | 3.6232 | 0.5625 |
| No log | 5.0 | 170 | 4.2950 | 0.5 |
| No log | 6.0 | 204 | 3.5435 | 0.5385 |
| No log | 7.0 | 238 | 4.1575 | 0.5 |
| No log | 8.0 | 272 | 3.9285 | 0.5 |
| No log | 9.0 | 306 | 3.8640 | 0.5 |
| No log | 10.0 | 340 | 3.9151 | 0.5 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 2023-10-10T02:57:46.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 0 | 2 | transformers | 2023-10-10T02:51:59 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1687
- F1: 0.6923
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.7383 | 0.6286 |
| No log | 2.0 | 68 | 1.7511 | 0.6923 |
| No log | 3.0 | 102 | 1.8099 | 0.6667 |
| No log | 4.0 | 136 | 2.0563 | 0.6667 |
| No log | 5.0 | 170 | 3.9454 | 0.6857 |
| No log | 6.0 | 204 | 2.7072 | 0.7333 |
| No log | 7.0 | 238 | 2.1334 | 0.6667 |
| No log | 8.0 | 272 | 2.1856 | 0.6897 |
| No log | 9.0 | 306 | 2.1742 | 0.6923 |
| No log | 10.0 | 340 | 2.1687 | 0.6923 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 2023-10-10T03:04:22.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 0 | 2 | transformers | 2023-10-10T02:57:43 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1053
- F1: 0.6154
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.1647 | 0.5600 |
| No log | 2.0 | 68 | 3.1840 | 0.6207 |
| No log | 3.0 | 102 | 2.6639 | 0.4762 |
| No log | 4.0 | 136 | 3.5473 | 0.5625 |
| No log | 5.0 | 170 | 3.4909 | 0.6061 |
| No log | 6.0 | 204 | 3.2699 | 0.4762 |
| No log | 7.0 | 238 | 4.6323 | 0.5294 |
| No log | 8.0 | 272 | 2.9648 | 0.5926 |
| No log | 9.0 | 306 | 3.1062 | 0.64 |
| No log | 10.0 | 340 | 3.1053 | 0.6154 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 2023-10-10T03:09:28.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 0 | 2 | transformers | 2023-10-10T03:03:44 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9269
- F1: 0.6471
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.4843 | 0.6857 |
| No log | 2.0 | 68 | 4.1622 | 0.6471 |
| No log | 3.0 | 102 | 3.1083 | 0.4800 |
| No log | 4.0 | 136 | 3.7705 | 0.6667 |
| No log | 5.0 | 170 | 4.4869 | 0.6000 |
| No log | 6.0 | 204 | 4.4586 | 0.6471 |
| No log | 7.0 | 238 | 4.0582 | 0.625 |
| No log | 8.0 | 272 | 3.7579 | 0.6667 |
| No log | 9.0 | 306 | 3.7097 | 0.6667 |
| No log | 10.0 | 340 | 3.9269 | 0.6471 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 2023-10-10T03:16:22.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 0 | 2 | transformers | 2023-10-10T03:09:06 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4704
- F1: 0.5000
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.7030 | 0.3810 |
| No log | 2.0 | 68 | 4.3539 | 0.6000 |
| No log | 3.0 | 102 | 2.6438 | 0.5217 |
| No log | 4.0 | 136 | 2.6505 | 0.5217 |
| No log | 5.0 | 170 | 3.2476 | 0.5385 |
| No log | 6.0 | 204 | 3.5014 | 0.5185 |
| No log | 7.0 | 238 | 3.9384 | 0.5517 |
| No log | 8.0 | 272 | 3.5222 | 0.5385 |
| No log | 9.0 | 306 | 3.4629 | 0.5000 |
| No log | 10.0 | 340 | 3.4704 | 0.5000 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 2023-10-10T03:21:21.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 0 | 2 | transformers | 2023-10-10T03:15:30 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6943
- F1: 0.7143
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.5346 | 0.7097 |
| No log | 2.0 | 68 | 2.4442 | 0.5556 |
| No log | 3.0 | 102 | 1.4723 | 0.7586 |
| No log | 4.0 | 136 | 2.3082 | 0.6667 |
| No log | 5.0 | 170 | 2.7745 | 0.6452 |
| No log | 6.0 | 204 | 3.7213 | 0.6452 |
| No log | 7.0 | 238 | 3.8197 | 0.625 |
| No log | 8.0 | 272 | 2.5555 | 0.7143 |
| No log | 9.0 | 306 | 2.7365 | 0.6897 |
| No log | 10.0 | 340 | 2.6943 | 0.7143 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 2023-10-10T03:28:20.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 0 | 2 | transformers | 2023-10-10T03:21:18 | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0984
- F1: 0.5000
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.8089 | 0.4444 |
| No log | 2.0 | 68 | 4.1142 | 0.4545 |
| No log | 3.0 | 102 | 3.6047 | 0.4762 |
| No log | 4.0 | 136 | 3.4138 | 0.5833 |
| No log | 5.0 | 170 | 3.5034 | 0.5833 |
| No log | 6.0 | 204 | 3.4997 | 0.6087 |
| No log | 7.0 | 238 | 4.0061 | 0.5000 |
| No log | 8.0 | 272 | 4.0838 | 0.5000 |
| No log | 9.0 | 306 | 4.0919 | 0.5000 |
| No log | 10.0 | 340 | 4.0984 | 0.5000 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 2023-10-10T03:33:07.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 0 | 2 | transformers | 2023-10-10T03:27:21 | ---
license: apache-2.0
base_model: Langboat/mengzi-bert-base-fin
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
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. -->
# mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
This model is a fine-tuned version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8155
- F1: 0.625
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.7052 | 0.6667 |
| No log | 2.0 | 68 | 4.1485 | 0.6452 |
| No log | 3.0 | 102 | 2.9633 | 0.6897 |
| No log | 4.0 | 136 | 3.0512 | 0.5833 |
| No log | 5.0 | 170 | 3.6109 | 0.6207 |
| No log | 6.0 | 204 | 3.9592 | 0.6000 |
| No log | 7.0 | 238 | 3.4112 | 0.5926 |
| No log | 8.0 | 272 | 3.3042 | 0.6429 |
| No log | 9.0 | 306 | 3.6594 | 0.5806 |
| No log | 10.0 | 340 | 3.8155 | 0.625 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-10T04:11:34.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-10T04:05:26 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6030
- F1: 0.4545
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.6832 | 0.6047 |
| No log | 2.0 | 68 | 0.4577 | 0.7500 |
| No log | 3.0 | 102 | 0.5035 | 0.6087 |
| No log | 4.0 | 136 | 0.4601 | 0.7826 |
| No log | 5.0 | 170 | 0.6560 | 0.5556 |
| No log | 6.0 | 204 | 0.6731 | 0.6667 |
| No log | 7.0 | 238 | 1.0705 | 0.4762 |
| No log | 8.0 | 272 | 1.3635 | 0.4762 |
| No log | 9.0 | 306 | 1.5471 | 0.4545 |
| No log | 10.0 | 340 | 1.6030 | 0.4545 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-10T04:12:56.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-10T04:06:47 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0995
- F1: 0.5
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.7304 | 0.5652 |
| No log | 2.0 | 68 | 0.5306 | 0.6087 |
| No log | 3.0 | 102 | 0.5015 | 0.7333 |
| No log | 4.0 | 136 | 1.0135 | 0.6471 |
| No log | 5.0 | 170 | 0.8160 | 0.5714 |
| No log | 6.0 | 204 | 1.3505 | 0.4800 |
| No log | 7.0 | 238 | 1.7370 | 0.5185 |
| No log | 8.0 | 272 | 2.0676 | 0.5517 |
| No log | 9.0 | 306 | 2.2369 | 0.5517 |
| No log | 10.0 | 340 | 2.0995 | 0.5 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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toobiza/table-transformer-restful-salad-71 | 2023-10-10T04:39:23.000Z | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | object-detection | toobiza | null | null | toobiza/table-transformer-restful-salad-71 | 0 | 2 | transformers | 2023-10-10T04:08:53 | ---
base_model: toobiza/table-transformer-spring-energy-46
tags:
- generated_from_trainer
model-index:
- name: table-transformer-restful-salad-71
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. -->
# table-transformer-restful-salad-71
This model is a fine-tuned version of [toobiza/table-transformer-spring-energy-46](https://huggingface.co/toobiza/table-transformer-spring-energy-46) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5430
- eval_loss_ce: 0.0000
- eval_loss_bbox: 0.0773
- eval_cardinality_error: 1.0
- eval_giou: 92.1861
- eval_runtime: 259.3258
- eval_samples_per_second: 2.63
- eval_steps_per_second: 1.315
- epoch: 1.0
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-10T04:23:47.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-10T04:17:42 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1733
- F1: 0.4348
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.7101 | 0.6875 |
| No log | 2.0 | 68 | 1.4494 | 0.4211 |
| No log | 3.0 | 102 | 1.4812 | 0.6429 |
| No log | 4.0 | 136 | 1.8322 | 0.5217 |
| No log | 5.0 | 170 | 1.6784 | 0.6429 |
| No log | 6.0 | 204 | 2.1127 | 0.4800 |
| No log | 7.0 | 238 | 2.3182 | 0.4348 |
| No log | 8.0 | 272 | 2.0711 | 0.4348 |
| No log | 9.0 | 306 | 2.1416 | 0.4348 |
| No log | 10.0 | 340 | 2.1733 | 0.4348 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-10T04:25:09.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-10T04:19:08 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0977
- F1: 0.6207
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.9010 | 0.6667 |
| No log | 2.0 | 68 | 1.1180 | 0.6 |
| No log | 3.0 | 102 | 1.8392 | 0.625 |
| No log | 4.0 | 136 | 1.7039 | 0.6087 |
| No log | 5.0 | 170 | 2.2346 | 0.5714 |
| No log | 6.0 | 204 | 2.2570 | 0.5714 |
| No log | 7.0 | 238 | 2.4359 | 0.5714 |
| No log | 8.0 | 272 | 2.0164 | 0.5926 |
| No log | 9.0 | 306 | 2.1606 | 0.6207 |
| No log | 10.0 | 340 | 2.0977 | 0.6207 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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princeton-nlp/SWE-Llama-7b | 2023-10-28T22:33:29.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2308.12950",
"arxiv:2310.06770",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | princeton-nlp | null | null | princeton-nlp/SWE-Llama-7b | 1 | 2 | transformers | 2023-10-10T04:37:28 | language: en
datasets:
- 37 popular Python code repositories
- See princeton-nlp/SWE-bench train split
- See the `make_datasets` documentation on SWE-bench's [GitHub](https://github.com/princeton-nlp/SWE-bench/tree/main/inference/make_datasets) for details on formatting input.
---
# SWE-Llama
SWE-Llama are variants of the [CodeLlama](https://arxiv.org/abs/2308.12950) model fine-tuned on software engineering tasks extracted from real-world GitHub issues and pull requests. They were introduced and evaluated on the SWE-bench benchmark in this [paper](https://arxiv.org/abs/2310.06770).
## Model Details
- **Architecture:** Transformer, based on [CodeLlama](https://arxiv.org/abs/2308.12950) architecture
- **Parameters:** 7 billion for SWE-Llama-7b, 13 billion for SWE-Llama-13b
- **Objective:** Generating patches to resolve GitHub issues, conditioned on issue description and code context
## Training Data
SWE-Llama was fine-tuned on 19,000 issues and pull requests collected from 37 popular Python code repositories on GitHub, disjoint from those used in SWE-bench.
## Training Procedure
- Fine-tuned only the attention matrices using LoRA method
- Trained for 4 epochs with a batch size of 32
- Selected best checkpoint based on validation perplexity
## Evaluation Results
When evaluated on the SWE-bench benchmark:
- SWE-Llama-7b achieved 3.0% issue resolution rate using oracle context retrieval
- SWE-Llama-13b achieved 4.0% issue resolution rate using oracle context retrieval
## BibTeX Entry
```tex
@misc{jimenez2023swebench,
title={SWE-bench: Can Language Models Resolve Real-World GitHub Issues?},
author={Carlos E. Jimenez and John Yang
and Alexander Wettig and Shunyu Yao
and Kexin Pei and Ofir Press and Karthik Narasimhan},
year={2023},
eprint={2310.06770},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,896 | [
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 2023-10-10T04:51:07.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 0 | 2 | transformers | 2023-10-10T04:44:20 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4779
- F1: 0.5517
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.3085 | 0.6154 |
| No log | 2.0 | 68 | 1.8856 | 0.6667 |
| No log | 3.0 | 102 | 2.8910 | 0.5185 |
| No log | 4.0 | 136 | 2.8716 | 0.5882 |
| No log | 5.0 | 170 | 3.0663 | 0.5 |
| No log | 6.0 | 204 | 2.7044 | 0.5385 |
| No log | 7.0 | 238 | 3.2759 | 0.5 |
| No log | 8.0 | 272 | 3.2155 | 0.5 |
| No log | 9.0 | 306 | 3.4729 | 0.5517 |
| No log | 10.0 | 340 | 3.4779 | 0.5517 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-10T05:00:23.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-10T04:54:19 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0360
- F1: 0.4800
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.3184 | 0.5926 |
| No log | 2.0 | 68 | 1.9505 | 0.6 |
| No log | 3.0 | 102 | 2.4287 | 0.4706 |
| No log | 4.0 | 136 | 2.9002 | 0.4167 |
| No log | 5.0 | 170 | 2.5180 | 0.5833 |
| No log | 6.0 | 204 | 2.4730 | 0.5455 |
| No log | 7.0 | 238 | 2.6034 | 0.5455 |
| No log | 8.0 | 272 | 3.0588 | 0.4615 |
| No log | 9.0 | 306 | 3.0563 | 0.4800 |
| No log | 10.0 | 340 | 3.0360 | 0.4800 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-10T05:03:10.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-10T04:57:09 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8105
- F1: 0.5600
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.9888 | 0.5185 |
| No log | 2.0 | 68 | 3.0889 | 0.5517 |
| No log | 3.0 | 102 | 2.6245 | 0.6429 |
| No log | 4.0 | 136 | 2.6655 | 0.5517 |
| No log | 5.0 | 170 | 2.1956 | 0.6364 |
| No log | 6.0 | 204 | 3.0515 | 0.5714 |
| No log | 7.0 | 238 | 3.2714 | 0.5714 |
| No log | 8.0 | 272 | 2.9990 | 0.5385 |
| No log | 9.0 | 306 | 2.8794 | 0.5600 |
| No log | 10.0 | 340 | 2.8105 | 0.5600 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 2023-10-10T05:16:05.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 0 | 2 | transformers | 2023-10-10T05:09:16 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2640
- F1: 0.6923
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.8918 | 0.6667 |
| No log | 2.0 | 68 | 1.8309 | 0.6667 |
| No log | 3.0 | 102 | 2.6245 | 0.5882 |
| No log | 4.0 | 136 | 2.7337 | 0.625 |
| No log | 5.0 | 170 | 2.5478 | 0.5806 |
| No log | 6.0 | 204 | 1.9587 | 0.6923 |
| No log | 7.0 | 238 | 2.2802 | 0.6429 |
| No log | 8.0 | 272 | 2.2421 | 0.6923 |
| No log | 9.0 | 306 | 2.2526 | 0.6923 |
| No log | 10.0 | 340 | 2.2640 | 0.6923 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 2023-10-10T05:24:44.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 0 | 2 | transformers | 2023-10-10T05:18:49 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2672
- F1: 0.6667
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 5.5615 | 0.6190 |
| No log | 2.0 | 68 | 2.1394 | 0.6429 |
| No log | 3.0 | 102 | 2.3368 | 0.6 |
| No log | 4.0 | 136 | 3.2086 | 0.5263 |
| No log | 5.0 | 170 | 2.5878 | 0.5385 |
| No log | 6.0 | 204 | 2.3890 | 0.6154 |
| No log | 7.0 | 238 | 2.4717 | 0.6000 |
| No log | 8.0 | 272 | 2.4050 | 0.5600 |
| No log | 9.0 | 306 | 2.2968 | 0.6667 |
| No log | 10.0 | 340 | 2.2672 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 2023-10-10T05:28:57.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 0 | 2 | transformers | 2023-10-10T05:22:12 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9481
- F1: 0.64
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.3469 | 0.6957 |
| No log | 2.0 | 68 | 3.0746 | 0.5833 |
| No log | 3.0 | 102 | 2.7399 | 0.6667 |
| No log | 4.0 | 136 | 3.3286 | 0.6667 |
| No log | 5.0 | 170 | 2.3420 | 0.6923 |
| No log | 6.0 | 204 | 2.3266 | 0.6957 |
| No log | 7.0 | 238 | 2.6646 | 0.6667 |
| No log | 8.0 | 272 | 2.6970 | 0.6923 |
| No log | 9.0 | 306 | 2.7228 | 0.6923 |
| No log | 10.0 | 340 | 2.9481 | 0.64 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 2023-10-10T05:36:55.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 0 | 2 | transformers | 2023-10-10T05:30:55 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7186
- F1: 0.5385
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.2330 | 0.6207 |
| No log | 2.0 | 68 | 3.2755 | 0.4706 |
| No log | 3.0 | 102 | 2.2638 | 0.64 |
| No log | 4.0 | 136 | 2.8411 | 0.625 |
| No log | 5.0 | 170 | 2.7943 | 0.5600 |
| No log | 6.0 | 204 | 4.5754 | 0.4615 |
| No log | 7.0 | 238 | 4.4887 | 0.6000 |
| No log | 8.0 | 272 | 4.5572 | 0.5 |
| No log | 9.0 | 306 | 3.7413 | 0.5385 |
| No log | 10.0 | 340 | 3.7186 | 0.5385 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 2023-10-10T05:41:59.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 0 | 2 | transformers | 2023-10-10T05:35:02 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8744
- F1: 0.6154
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.0994 | 0.6429 |
| No log | 2.0 | 68 | 1.8310 | 0.8 |
| No log | 3.0 | 102 | 2.1470 | 0.6957 |
| No log | 4.0 | 136 | 3.2126 | 0.6207 |
| No log | 5.0 | 170 | 2.4359 | 0.6667 |
| No log | 6.0 | 204 | 2.3271 | 0.6667 |
| No log | 7.0 | 238 | 2.5015 | 0.6154 |
| No log | 8.0 | 272 | 2.6520 | 0.6154 |
| No log | 9.0 | 306 | 2.8870 | 0.6154 |
| No log | 10.0 | 340 | 2.8744 | 0.6154 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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m-aliabbas1/mva_ner_2 | 2023-10-10T05:39:00.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | m-aliabbas1 | null | null | m-aliabbas1/mva_ner_2 | 0 | 2 | transformers | 2023-10-10T05:38:53 | ---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: mva_ner_2
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. -->
# mva_ner_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Overall Precision: 0.9873
- Overall Recall: 0.9873
- Overall F1: 0.9873
- Overall Accuracy: 0.9987
- Year F1: 1.0
- Years Ago F1: 0.9844
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Year F1 | Years Ago F1 |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------:|:------------:|
| 0.0099 | 35.71 | 1000 | 0.0225 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 |
| 0.0078 | 71.43 | 2000 | 0.0157 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 |
| 0.0078 | 107.14 | 3000 | 0.0075 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0061 | 142.86 | 4000 | 0.0062 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0053 | 178.57 | 5000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0049 | 214.29 | 6000 | 0.0179 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 |
| 0.0049 | 250.0 | 7000 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0034 | 285.71 | 8000 | 0.0064 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 |
| 0.0037 | 321.43 | 9000 | 0.0148 | 0.9875 | 1.0 | 0.9937 | 0.9987 | 1.0 | 0.9922 |
| 0.0035 | 357.14 | 10000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.003 | 392.86 | 11000 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0028 | 428.57 | 12000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0025 | 464.29 | 13000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0024 | 500.0 | 14000 | 0.0026 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 2023-10-10T05:49:19.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 0 | 2 | transformers | 2023-10-10T05:43:10 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5429
- F1: 0.5926
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.2518 | 0.5926 |
| No log | 2.0 | 68 | 3.3509 | 0.5926 |
| No log | 3.0 | 102 | 3.5787 | 0.5000 |
| No log | 4.0 | 136 | 2.6393 | 0.6897 |
| No log | 5.0 | 170 | 2.5314 | 0.6154 |
| No log | 6.0 | 204 | 3.4185 | 0.5926 |
| No log | 7.0 | 238 | 3.3706 | 0.5926 |
| No log | 8.0 | 272 | 3.5516 | 0.5600 |
| No log | 9.0 | 306 | 3.5445 | 0.6154 |
| No log | 10.0 | 340 | 3.5429 | 0.5926 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Alnusjaponica/toxicity-classification-multi-class | 2023-10-10T05:55:26.000Z | [
"transformers",
"pytorch",
"distilbert",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | Alnusjaponica | null | null | Alnusjaponica/toxicity-classification-multi-class | 0 | 2 | transformers | 2023-10-10T05:47:10 | ---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
model-index:
- name: toxicity-classification-multi-class
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. -->
# toxicity-classification-multi-class
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the [Japanese toxicity dataset](https://github.com/inspection-ai/japanese-toxic-dataset/tree/main).
It achieves the following results on the evaluation set:
- Loss: 0.2642
- Roc Auc: 0.8447
## 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: 8.133692392125703e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.0
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 2023-10-10T05:55:12.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 0 | 2 | transformers | 2023-10-10T05:47:59 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8857
- F1: 0.6000
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.9585 | 0.6452 |
| No log | 2.0 | 68 | 4.8595 | 0.6207 |
| No log | 3.0 | 102 | 4.2012 | 0.6000 |
| No log | 4.0 | 136 | 3.0600 | 0.5926 |
| No log | 5.0 | 170 | 2.9014 | 0.6154 |
| No log | 6.0 | 204 | 2.7731 | 0.6923 |
| No log | 7.0 | 238 | 3.0148 | 0.6452 |
| No log | 8.0 | 272 | 2.7014 | 0.6429 |
| No log | 9.0 | 306 | 2.9877 | 0.6000 |
| No log | 10.0 | 340 | 2.8857 | 0.6000 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 2023-10-10T06:01:53.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 0 | 2 | transformers | 2023-10-10T05:55:25 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
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. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9017
- F1: 0.6667
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 4.4872 | 0.6286 |
| No log | 2.0 | 68 | 2.0803 | 0.6667 |
| No log | 3.0 | 102 | 1.9813 | 0.6429 |
| No log | 4.0 | 136 | 2.0595 | 0.7143 |
| No log | 5.0 | 170 | 2.4282 | 0.6154 |
| No log | 6.0 | 204 | 2.5838 | 0.6875 |
| No log | 7.0 | 238 | 2.5736 | 0.7143 |
| No log | 8.0 | 272 | 2.8531 | 0.6667 |
| No log | 9.0 | 306 | 2.8916 | 0.6667 |
| No log | 10.0 | 340 | 2.9017 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 2023-10-10T06:07:19.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v10 | 0 | 2 | transformers | 2023-10-10T06:01:12 | ---
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
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. -->
# chinese-lert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v10
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3985
- F1: 0.6207
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.2521 | 0.7200 |
| No log | 2.0 | 68 | 3.3694 | 0.7200 |
| No log | 3.0 | 102 | 3.3089 | 0.7200 |
| No log | 4.0 | 136 | 3.2982 | 0.5714 |
| No log | 5.0 | 170 | 3.8089 | 0.6667 |
| No log | 6.0 | 204 | 3.1578 | 0.6667 |
| No log | 7.0 | 238 | 3.2455 | 0.6207 |
| No log | 8.0 | 272 | 3.2418 | 0.6207 |
| No log | 9.0 | 306 | 3.3856 | 0.6207 |
| No log | 10.0 | 340 | 3.3985 | 0.6207 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-10T06:25:30.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-10T06:19:28 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
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. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- F1: 0.8276
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.6823 | 0.0 |
| No log | 2.0 | 68 | 0.6171 | 0.1429 |
| No log | 3.0 | 102 | 0.6822 | 0.0 |
| No log | 4.0 | 136 | 0.6761 | 0.0 |
| No log | 5.0 | 170 | 0.6323 | 0.7857 |
| No log | 6.0 | 204 | 0.7028 | 0.6047 |
| No log | 7.0 | 238 | 0.4707 | 0.7500 |
| No log | 8.0 | 272 | 0.4489 | 0.7857 |
| No log | 9.0 | 306 | 0.4544 | 0.8000 |
| No log | 10.0 | 340 | 0.4279 | 0.8276 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-10T06:37:36.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-10T06:31:45 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9297
- F1: 0.7333
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.5485 | 0.7500 |
| No log | 2.0 | 68 | 0.5388 | 0.6667 |
| No log | 3.0 | 102 | 0.5299 | 0.7857 |
| No log | 4.0 | 136 | 0.7368 | 0.7407 |
| No log | 5.0 | 170 | 0.9532 | 0.7097 |
| No log | 6.0 | 204 | 0.8788 | 0.7333 |
| No log | 7.0 | 238 | 0.8768 | 0.7333 |
| No log | 8.0 | 272 | 0.8797 | 0.6667 |
| No log | 9.0 | 306 | 0.9283 | 0.7333 |
| No log | 10.0 | 340 | 0.9297 | 0.7333 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 2023-10-10T06:49:48.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 0 | 2 | transformers | 2023-10-10T06:43:52 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
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. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9065
- F1: 0.7586
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.6880 | 0.7586 |
| No log | 2.0 | 68 | 0.6591 | 0.7000 |
| No log | 3.0 | 102 | 0.8068 | 0.7200 |
| No log | 4.0 | 136 | 0.9149 | 0.7143 |
| No log | 5.0 | 170 | 0.6697 | 0.7857 |
| No log | 6.0 | 204 | 0.8876 | 0.6364 |
| No log | 7.0 | 238 | 0.7585 | 0.7857 |
| No log | 8.0 | 272 | 0.7230 | 0.7857 |
| No log | 9.0 | 306 | 0.8959 | 0.7586 |
| No log | 10.0 | 340 | 0.9065 | 0.7586 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 2023-10-10T07:01:56.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 0 | 2 | transformers | 2023-10-10T06:55:56 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
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. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2448
- F1: 0.7143
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.8330 | 0.7407 |
| No log | 2.0 | 68 | 0.7893 | 0.7857 |
| No log | 3.0 | 102 | 1.2662 | 0.5217 |
| No log | 4.0 | 136 | 1.2171 | 0.64 |
| No log | 5.0 | 170 | 0.9640 | 0.7407 |
| No log | 6.0 | 204 | 1.0364 | 0.7742 |
| No log | 7.0 | 238 | 1.3804 | 0.6154 |
| No log | 8.0 | 272 | 1.4683 | 0.6154 |
| No log | 9.0 | 306 | 1.2387 | 0.7143 |
| No log | 10.0 | 340 | 1.2448 | 0.7143 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-10T07:14:01.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-10T07:07:59 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
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. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1743
- F1: 0.7200
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 1.0640 | 0.6667 |
| No log | 2.0 | 68 | 1.0504 | 0.6923 |
| No log | 3.0 | 102 | 0.9497 | 0.7742 |
| No log | 4.0 | 136 | 0.8242 | 0.8000 |
| No log | 5.0 | 170 | 0.8966 | 0.7200 |
| No log | 6.0 | 204 | 1.0735 | 0.6923 |
| No log | 7.0 | 238 | 1.1393 | 0.6957 |
| No log | 8.0 | 272 | 1.2874 | 0.6923 |
| No log | 9.0 | 306 | 1.1630 | 0.7200 |
| No log | 10.0 | 340 | 1.1743 | 0.7200 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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iamnguyen/BERT-70M-Sentence-Transformer | 2023-10-10T07:16:31.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | iamnguyen | null | null | iamnguyen/BERT-70M-Sentence-Transformer | 0 | 2 | sentence-transformers | 2023-10-10T07:13:10 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4508 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,879 | [
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hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 2023-10-10T07:26:07.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 0 | 2 | transformers | 2023-10-10T07:20:04 | ---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
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. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7126
- F1: 0.64
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 1.1056 | 0.7742 |
| No log | 2.0 | 68 | 0.8684 | 0.7692 |
| No log | 3.0 | 102 | 1.2911 | 0.6923 |
| No log | 4.0 | 136 | 1.4857 | 0.64 |
| No log | 5.0 | 170 | 1.4399 | 0.6667 |
| No log | 6.0 | 204 | 1.6329 | 0.64 |
| No log | 7.0 | 238 | 1.6839 | 0.64 |
| No log | 8.0 | 272 | 1.6776 | 0.64 |
| No log | 9.0 | 306 | 1.7189 | 0.64 |
| No log | 10.0 | 340 | 1.7126 | 0.64 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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SHENMU007/neunit_BASE_V9.5.15 | 2023-10-10T08:32:33.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | SHENMU007 | null | null | SHENMU007/neunit_BASE_V9.5.15 | 0 | 2 | transformers | 2023-10-10T07:29:20 | ---
language:
- zh
license: mit
base_model: microsoft/speecht5_tts
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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DopeorNope/zerocoka7_adpt | 2023-10-10T08:26:42.000Z | [
"peft",
"pytorch",
"region:us"
] | null | DopeorNope | null | null | DopeorNope/zerocoka7_adpt | 0 | 2 | peft | 2023-10-10T08:26:19 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
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SHENMU007/neunit_BASE_V9.6.1 | 2023-10-10T15:28:01.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | SHENMU007 | null | null | SHENMU007/neunit_BASE_V9.6.1 | 0 | 2 | transformers | 2023-10-10T09:35:50 | ---
language:
- zh
license: mit
base_model: microsoft/speecht5_tts
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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lht1107/distilbert-base-uncased-finetuned-emotion | 2023-10-10T12:12:19.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | lht1107 | null | null | lht1107/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-10T11:08:48 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.929
- name: F1
type: f1
value: 0.9289651135784346
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2066
- Accuracy: 0.929
- F1: 0.9290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7843 | 1.0 | 250 | 0.2875 | 0.913 | 0.9122 |
| 0.2373 | 2.0 | 500 | 0.2066 | 0.929 | 0.9290 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tommert25/robbert1010_lrate7.5b8 | 2023-10-10T15:06:04.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert1010_lrate7.5b8 | 0 | 2 | transformers | 2023-10-10T11:51:57 | ---
license: mit
base_model: Tommert25/robbert1010_lrate7.5b8
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert1010_lrate7.5b8
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. -->
# robbert1010_lrate7.5b8
This model is a fine-tuned version of [Tommert25/robbert1010_lrate7.5b8](https://huggingface.co/Tommert25/robbert1010_lrate7.5b8) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6507
- Precisions: 0.8092
- Recall: 0.7854
- F-measure: 0.7957
- Accuracy: 0.9171
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.0839 | 1.0 | 471 | 0.5608 | 0.8220 | 0.7659 | 0.7824 | 0.9082 |
| 0.0762 | 2.0 | 942 | 0.5485 | 0.8290 | 0.7683 | 0.7743 | 0.9086 |
| 0.0412 | 3.0 | 1413 | 0.6303 | 0.7841 | 0.7745 | 0.7780 | 0.9090 |
| 0.0352 | 4.0 | 1884 | 0.6346 | 0.7978 | 0.7831 | 0.7893 | 0.9109 |
| 0.0251 | 5.0 | 2355 | 0.6494 | 0.7956 | 0.7878 | 0.7911 | 0.9138 |
| 0.0138 | 6.0 | 2826 | 0.6507 | 0.8092 | 0.7854 | 0.7957 | 0.9171 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Suksuma/distilbert-base-multilingual-cased-finetuned-imdb | 2023-10-10T13:23:14.000Z | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | Suksuma | null | null | Suksuma/distilbert-base-multilingual-cased-finetuned-imdb | 0 | 2 | transformers | 2023-10-10T12:56:00 | ---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-multilingual-cased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4923
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8485 | 1.0 | 2500 | 2.6147 |
| 2.6629 | 2.0 | 5000 | 2.5229 |
| 2.5686 | 3.0 | 7500 | 2.4958 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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vedantjumle/bert-clinc150-1 | 2023-10-10T15:54:47.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | vedantjumle | null | null | vedantjumle/bert-clinc150-1 | 0 | 2 | transformers | 2023-10-10T14:42:08 | ---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/bert-clinc150-1
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. -->
# vedantjumle/bert-clinc150-1
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1204
- Validation Loss: 0.1378
- Train Accuracy: 0.9733
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 12640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.6008 | 0.5869 | 0.9613 | 0 |
| 0.3623 | 0.1901 | 0.9716 | 1 |
| 0.1204 | 0.1378 | 0.9733 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tommert25/robbert1010_lrate7.5b4 | 2023-10-10T15:37:08.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/robbert1010_lrate7.5b4 | 0 | 2 | transformers | 2023-10-10T15:07:21 | ---
license: mit
base_model: Tommert25/robbert0410_lrate7.5b4
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert1010_lrate7.5b4
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. -->
# robbert1010_lrate7.5b4
This model is a fine-tuned version of [Tommert25/robbert0410_lrate7.5b4](https://huggingface.co/Tommert25/robbert0410_lrate7.5b4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6951
- Precisions: 0.8407
- Recall: 0.8357
- F-measure: 0.8369
- Accuracy: 0.9176
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.3016 | 1.0 | 942 | 0.4329 | 0.8299 | 0.7433 | 0.7703 | 0.8953 |
| 0.2107 | 2.0 | 1884 | 0.5107 | 0.7934 | 0.7827 | 0.7878 | 0.9055 |
| 0.154 | 3.0 | 2826 | 0.7341 | 0.8129 | 0.7687 | 0.7758 | 0.8935 |
| 0.1198 | 4.0 | 3768 | 0.7313 | 0.7973 | 0.7394 | 0.7559 | 0.8993 |
| 0.0928 | 5.0 | 4710 | 0.6702 | 0.7996 | 0.7765 | 0.7824 | 0.8990 |
| 0.0529 | 6.0 | 5652 | 0.7075 | 0.8262 | 0.7983 | 0.8099 | 0.9106 |
| 0.039 | 7.0 | 6594 | 0.7946 | 0.8470 | 0.7670 | 0.7899 | 0.9087 |
| 0.0259 | 8.0 | 7536 | 0.6951 | 0.8407 | 0.8357 | 0.8369 | 0.9176 |
| 0.0203 | 9.0 | 8478 | 0.6762 | 0.8380 | 0.8210 | 0.8285 | 0.9206 |
| 0.0181 | 10.0 | 9420 | 0.6993 | 0.8384 | 0.8206 | 0.8289 | 0.9190 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
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