End of training
Browse files- README.md +45 -179
- all_results.json +21 -0
- config.json +1 -1
- eval_results.json +11 -0
- train_results.json +8 -0
- trainer_state.json +230 -0
- training_args.bin +3 -0
README.md
CHANGED
@@ -1,199 +1,65 @@
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
---
|
5 |
|
6 |
-
|
|
|
7 |
|
8 |
-
|
9 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
10 |
|
|
|
11 |
|
12 |
-
|
13 |
|
14 |
-
|
15 |
|
16 |
-
|
17 |
|
18 |
-
|
19 |
|
20 |
-
|
21 |
-
- **Funded by [optional]:** [More Information Needed]
|
22 |
-
- **Shared by [optional]:** [More Information Needed]
|
23 |
-
- **Model type:** [More Information Needed]
|
24 |
-
- **Language(s) (NLP):** [More Information Needed]
|
25 |
-
- **License:** [More Information Needed]
|
26 |
-
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
|
28 |
-
|
29 |
|
30 |
-
|
31 |
|
32 |
-
|
33 |
-
-
|
34 |
-
-
|
|
|
|
|
|
|
|
|
|
|
|
|
35 |
|
36 |
-
|
37 |
|
38 |
-
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
|
40 |
-
### Direct Use
|
41 |
|
42 |
-
|
43 |
|
44 |
-
|
45 |
-
|
46 |
-
|
47 |
-
|
48 |
-
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
49 |
-
|
50 |
-
[More Information Needed]
|
51 |
-
|
52 |
-
### Out-of-Scope Use
|
53 |
-
|
54 |
-
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
-
|
56 |
-
[More Information Needed]
|
57 |
-
|
58 |
-
## Bias, Risks, and Limitations
|
59 |
-
|
60 |
-
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
-
|
62 |
-
[More Information Needed]
|
63 |
-
|
64 |
-
### Recommendations
|
65 |
-
|
66 |
-
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
-
|
68 |
-
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
69 |
-
|
70 |
-
## How to Get Started with the Model
|
71 |
-
|
72 |
-
Use the code below to get started with the model.
|
73 |
-
|
74 |
-
[More Information Needed]
|
75 |
-
|
76 |
-
## Training Details
|
77 |
-
|
78 |
-
### Training Data
|
79 |
-
|
80 |
-
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
81 |
-
|
82 |
-
[More Information Needed]
|
83 |
-
|
84 |
-
### Training Procedure
|
85 |
-
|
86 |
-
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
87 |
-
|
88 |
-
#### Preprocessing [optional]
|
89 |
-
|
90 |
-
[More Information Needed]
|
91 |
-
|
92 |
-
|
93 |
-
#### Training Hyperparameters
|
94 |
-
|
95 |
-
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
-
|
97 |
-
#### Speeds, Sizes, Times [optional]
|
98 |
-
|
99 |
-
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
-
|
101 |
-
[More Information Needed]
|
102 |
-
|
103 |
-
## Evaluation
|
104 |
-
|
105 |
-
<!-- This section describes the evaluation protocols and provides the results. -->
|
106 |
-
|
107 |
-
### Testing Data, Factors & Metrics
|
108 |
-
|
109 |
-
#### Testing Data
|
110 |
-
|
111 |
-
<!-- This should link to a Dataset Card if possible. -->
|
112 |
-
|
113 |
-
[More Information Needed]
|
114 |
-
|
115 |
-
#### Factors
|
116 |
-
|
117 |
-
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
118 |
-
|
119 |
-
[More Information Needed]
|
120 |
-
|
121 |
-
#### Metrics
|
122 |
-
|
123 |
-
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
124 |
-
|
125 |
-
[More Information Needed]
|
126 |
-
|
127 |
-
### Results
|
128 |
-
|
129 |
-
[More Information Needed]
|
130 |
-
|
131 |
-
#### Summary
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
## Model Examination [optional]
|
136 |
-
|
137 |
-
<!-- Relevant interpretability work for the model goes here -->
|
138 |
-
|
139 |
-
[More Information Needed]
|
140 |
-
|
141 |
-
## Environmental Impact
|
142 |
-
|
143 |
-
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
-
|
145 |
-
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).
|
146 |
-
|
147 |
-
- **Hardware Type:** [More Information Needed]
|
148 |
-
- **Hours used:** [More Information Needed]
|
149 |
-
- **Cloud Provider:** [More Information Needed]
|
150 |
-
- **Compute Region:** [More Information Needed]
|
151 |
-
- **Carbon Emitted:** [More Information Needed]
|
152 |
-
|
153 |
-
## Technical Specifications [optional]
|
154 |
-
|
155 |
-
### Model Architecture and Objective
|
156 |
-
|
157 |
-
[More Information Needed]
|
158 |
-
|
159 |
-
### Compute Infrastructure
|
160 |
-
|
161 |
-
[More Information Needed]
|
162 |
-
|
163 |
-
#### Hardware
|
164 |
-
|
165 |
-
[More Information Needed]
|
166 |
-
|
167 |
-
#### Software
|
168 |
-
|
169 |
-
[More Information Needed]
|
170 |
-
|
171 |
-
## Citation [optional]
|
172 |
-
|
173 |
-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
-
|
175 |
-
**BibTeX:**
|
176 |
-
|
177 |
-
[More Information Needed]
|
178 |
-
|
179 |
-
**APA:**
|
180 |
-
|
181 |
-
[More Information Needed]
|
182 |
-
|
183 |
-
## Glossary [optional]
|
184 |
-
|
185 |
-
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
-
|
187 |
-
[More Information Needed]
|
188 |
-
|
189 |
-
## More Information [optional]
|
190 |
-
|
191 |
-
[More Information Needed]
|
192 |
-
|
193 |
-
## Model Card Authors [optional]
|
194 |
-
|
195 |
-
[More Information Needed]
|
196 |
-
|
197 |
-
## Model Card Contact
|
198 |
-
|
199 |
-
[More Information Needed]
|
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
+
license: apache-2.0
|
4 |
+
base_model: PlanTL-GOB-ES/RoBERTalex
|
5 |
+
tags:
|
6 |
+
- generated_from_trainer
|
7 |
+
metrics:
|
8 |
+
- accuracy
|
9 |
+
- f1
|
10 |
+
- precision
|
11 |
+
- recall
|
12 |
+
model-index:
|
13 |
+
- name: RoBertaLex_v10
|
14 |
+
results: []
|
15 |
---
|
16 |
|
17 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
18 |
+
should probably proofread and complete it, then remove this comment. -->
|
19 |
|
20 |
+
# RoBertaLex_v10
|
21 |
|
22 |
+
This model is a fine-tuned version of [PlanTL-GOB-ES/RoBERTalex](https://huggingface.co/PlanTL-GOB-ES/RoBERTalex) on the None dataset.
|
23 |
+
It achieves the following results on the evaluation set:
|
24 |
+
- Accuracy: 0.8979
|
25 |
+
- F1: 0.8975
|
26 |
+
- Precision: 0.8983
|
27 |
+
- Recall: 0.8982
|
28 |
+
- Loss: 0.4829
|
29 |
|
30 |
+
## Model description
|
31 |
|
32 |
+
More information needed
|
33 |
|
34 |
+
## Intended uses & limitations
|
35 |
|
36 |
+
More information needed
|
37 |
|
38 |
+
## Training and evaluation data
|
39 |
|
40 |
+
More information needed
|
|
|
|
|
|
|
|
|
|
|
|
|
41 |
|
42 |
+
## Training procedure
|
43 |
|
44 |
+
### Training hyperparameters
|
45 |
|
46 |
+
The following hyperparameters were used during training:
|
47 |
+
- learning_rate: 2e-05
|
48 |
+
- train_batch_size: 8
|
49 |
+
- eval_batch_size: 8
|
50 |
+
- seed: 42
|
51 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
52 |
+
- lr_scheduler_type: cosine
|
53 |
+
- lr_scheduler_warmup_steps: 500
|
54 |
+
- num_epochs: 12
|
55 |
|
56 |
+
### Training results
|
57 |
|
|
|
58 |
|
|
|
59 |
|
60 |
+
### Framework versions
|
61 |
|
62 |
+
- Transformers 4.44.2
|
63 |
+
- Pytorch 2.5.0+cu121
|
64 |
+
- Datasets 3.1.0
|
65 |
+
- Tokenizers 0.19.1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
all_results.json
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 6.0,
|
3 |
+
"eval_accuracy": 0.8978835978835978,
|
4 |
+
"eval_f1": 0.8974625900835275,
|
5 |
+
"eval_loss": 0.48289522528648376,
|
6 |
+
"eval_precision": 0.8983196617688776,
|
7 |
+
"eval_recall": 0.8981742359077385,
|
8 |
+
"eval_runtime": 113.6522,
|
9 |
+
"eval_samples_per_second": 33.259,
|
10 |
+
"eval_steps_per_second": 4.162,
|
11 |
+
"total_flos": 1.39288377102336e+16,
|
12 |
+
"train_eval_accuracy": 0.9667800453514739,
|
13 |
+
"train_eval_f1": 0.9665532935289809,
|
14 |
+
"train_eval_loss": 0.13728319108486176,
|
15 |
+
"train_eval_precision": 0.9668769668275939,
|
16 |
+
"train_eval_recall": 0.9665372917440072,
|
17 |
+
"train_loss": 0.7601337173475748,
|
18 |
+
"train_runtime": 7219.9118,
|
19 |
+
"train_samples_per_second": 14.659,
|
20 |
+
"train_steps_per_second": 1.833
|
21 |
+
}
|
config.json
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
{
|
2 |
-
"_name_or_path": "
|
3 |
"architectures": [
|
4 |
"RobertaForSequenceClassification"
|
5 |
],
|
|
|
1 |
{
|
2 |
+
"_name_or_path": "PlanTL-GOB-ES/RoBERTalex",
|
3 |
"architectures": [
|
4 |
"RobertaForSequenceClassification"
|
5 |
],
|
eval_results.json
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 6.0,
|
3 |
+
"eval_accuracy": 0.8978835978835978,
|
4 |
+
"eval_f1": 0.8974625900835275,
|
5 |
+
"eval_loss": 0.48289522528648376,
|
6 |
+
"eval_precision": 0.8983196617688776,
|
7 |
+
"eval_recall": 0.8981742359077385,
|
8 |
+
"eval_runtime": 113.6522,
|
9 |
+
"eval_samples_per_second": 33.259,
|
10 |
+
"eval_steps_per_second": 4.162
|
11 |
+
}
|
train_results.json
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 6.0,
|
3 |
+
"total_flos": 1.39288377102336e+16,
|
4 |
+
"train_loss": 0.7601337173475748,
|
5 |
+
"train_runtime": 7219.9118,
|
6 |
+
"train_samples_per_second": 14.659,
|
7 |
+
"train_steps_per_second": 1.833
|
8 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,230 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"best_metric": null,
|
3 |
+
"best_model_checkpoint": null,
|
4 |
+
"epoch": 6.0,
|
5 |
+
"eval_steps": 500,
|
6 |
+
"global_step": 6618,
|
7 |
+
"is_hyper_param_search": false,
|
8 |
+
"is_local_process_zero": true,
|
9 |
+
"is_world_process_zero": true,
|
10 |
+
"log_history": [
|
11 |
+
{
|
12 |
+
"epoch": 1.0,
|
13 |
+
"step": 1103,
|
14 |
+
"train_eval_accuracy": 0.7038548752834467,
|
15 |
+
"train_eval_f1": 0.6632307531732782,
|
16 |
+
"train_eval_loss": 1.0848891735076904,
|
17 |
+
"train_eval_precision": 0.7168947655115439,
|
18 |
+
"train_eval_recall": 0.7035581415926491,
|
19 |
+
"train_loss": 1.0848891735076904,
|
20 |
+
"train_runtime": 264.7926,
|
21 |
+
"train_samples_per_second": 33.309,
|
22 |
+
"train_steps_per_second": 4.166
|
23 |
+
},
|
24 |
+
{
|
25 |
+
"epoch": 1.0,
|
26 |
+
"eval_accuracy": 0.6907407407407408,
|
27 |
+
"eval_f1": 0.650349099447679,
|
28 |
+
"eval_loss": 1.1183524131774902,
|
29 |
+
"eval_precision": 0.6921232992073287,
|
30 |
+
"eval_recall": 0.6915906491164756,
|
31 |
+
"eval_runtime": 113.5601,
|
32 |
+
"eval_samples_per_second": 33.286,
|
33 |
+
"eval_steps_per_second": 4.165,
|
34 |
+
"step": 1103
|
35 |
+
},
|
36 |
+
{
|
37 |
+
"epoch": 2.0,
|
38 |
+
"step": 2206,
|
39 |
+
"train_eval_accuracy": 0.8954648526077098,
|
40 |
+
"train_eval_f1": 0.8862104131213615,
|
41 |
+
"train_eval_loss": 0.3707798719406128,
|
42 |
+
"train_eval_precision": 0.9098024890839804,
|
43 |
+
"train_eval_recall": 0.8944939716644009,
|
44 |
+
"train_loss": 0.370779812335968,
|
45 |
+
"train_runtime": 265.6336,
|
46 |
+
"train_samples_per_second": 33.204,
|
47 |
+
"train_steps_per_second": 4.152
|
48 |
+
},
|
49 |
+
{
|
50 |
+
"epoch": 2.0,
|
51 |
+
"eval_accuracy": 0.8669312169312169,
|
52 |
+
"eval_f1": 0.8584639708159391,
|
53 |
+
"eval_loss": 0.4698329567909241,
|
54 |
+
"eval_precision": 0.8609250931703862,
|
55 |
+
"eval_recall": 0.8686962380593306,
|
56 |
+
"eval_runtime": 113.5652,
|
57 |
+
"eval_samples_per_second": 33.285,
|
58 |
+
"eval_steps_per_second": 4.165,
|
59 |
+
"step": 2206
|
60 |
+
},
|
61 |
+
{
|
62 |
+
"epoch": 3.0,
|
63 |
+
"step": 3309,
|
64 |
+
"train_eval_accuracy": 0.9257369614512472,
|
65 |
+
"train_eval_f1": 0.9252576803169037,
|
66 |
+
"train_eval_loss": 0.28054898977279663,
|
67 |
+
"train_eval_precision": 0.9277462662521301,
|
68 |
+
"train_eval_recall": 0.9257165963161775,
|
69 |
+
"train_loss": 0.28054898977279663,
|
70 |
+
"train_runtime": 265.9318,
|
71 |
+
"train_samples_per_second": 33.166,
|
72 |
+
"train_steps_per_second": 4.148
|
73 |
+
},
|
74 |
+
{
|
75 |
+
"epoch": 3.0,
|
76 |
+
"eval_accuracy": 0.8891534391534391,
|
77 |
+
"eval_f1": 0.8871334294657096,
|
78 |
+
"eval_loss": 0.43829405307769775,
|
79 |
+
"eval_precision": 0.8905971669580465,
|
80 |
+
"eval_recall": 0.8885704350266709,
|
81 |
+
"eval_runtime": 113.7207,
|
82 |
+
"eval_samples_per_second": 33.239,
|
83 |
+
"eval_steps_per_second": 4.159,
|
84 |
+
"step": 3309
|
85 |
+
},
|
86 |
+
{
|
87 |
+
"epoch": 4.0,
|
88 |
+
"step": 4412,
|
89 |
+
"train_eval_accuracy": 0.9454648526077097,
|
90 |
+
"train_eval_f1": 0.9451948595406918,
|
91 |
+
"train_eval_loss": 0.21954156458377838,
|
92 |
+
"train_eval_precision": 0.9465585461247004,
|
93 |
+
"train_eval_recall": 0.9453011434624647,
|
94 |
+
"train_loss": 0.21954156458377838,
|
95 |
+
"train_runtime": 265.1444,
|
96 |
+
"train_samples_per_second": 33.265,
|
97 |
+
"train_steps_per_second": 4.16
|
98 |
+
},
|
99 |
+
{
|
100 |
+
"epoch": 4.0,
|
101 |
+
"eval_accuracy": 0.9005291005291005,
|
102 |
+
"eval_f1": 0.8997221694148184,
|
103 |
+
"eval_loss": 0.4437304437160492,
|
104 |
+
"eval_precision": 0.9031201315235958,
|
105 |
+
"eval_recall": 0.900597065854027,
|
106 |
+
"eval_runtime": 113.9005,
|
107 |
+
"eval_samples_per_second": 33.187,
|
108 |
+
"eval_steps_per_second": 4.153,
|
109 |
+
"step": 4412
|
110 |
+
},
|
111 |
+
{
|
112 |
+
"epoch": 5.0,
|
113 |
+
"step": 5515,
|
114 |
+
"train_eval_accuracy": 0.9524943310657596,
|
115 |
+
"train_eval_f1": 0.9521538910810808,
|
116 |
+
"train_eval_loss": 0.18594056367874146,
|
117 |
+
"train_eval_precision": 0.9535301944285633,
|
118 |
+
"train_eval_recall": 0.9522655532984384,
|
119 |
+
"train_loss": 0.18594057857990265,
|
120 |
+
"train_runtime": 265.1404,
|
121 |
+
"train_samples_per_second": 33.265,
|
122 |
+
"train_steps_per_second": 4.16
|
123 |
+
},
|
124 |
+
{
|
125 |
+
"epoch": 5.0,
|
126 |
+
"eval_accuracy": 0.8957671957671958,
|
127 |
+
"eval_f1": 0.8944708569814356,
|
128 |
+
"eval_loss": 0.47566497325897217,
|
129 |
+
"eval_precision": 0.8970355161375609,
|
130 |
+
"eval_recall": 0.8955264074998366,
|
131 |
+
"eval_runtime": 113.9062,
|
132 |
+
"eval_samples_per_second": 33.185,
|
133 |
+
"eval_steps_per_second": 4.153,
|
134 |
+
"step": 5515
|
135 |
+
},
|
136 |
+
{
|
137 |
+
"epoch": 6.0,
|
138 |
+
"step": 6618,
|
139 |
+
"train_eval_accuracy": 0.9667800453514739,
|
140 |
+
"train_eval_f1": 0.9665532935289809,
|
141 |
+
"train_eval_loss": 0.13728319108486176,
|
142 |
+
"train_eval_precision": 0.9668769668275939,
|
143 |
+
"train_eval_recall": 0.9665372917440072,
|
144 |
+
"train_loss": 0.13728320598602295,
|
145 |
+
"train_runtime": 265.104,
|
146 |
+
"train_samples_per_second": 33.27,
|
147 |
+
"train_steps_per_second": 4.161
|
148 |
+
},
|
149 |
+
{
|
150 |
+
"epoch": 6.0,
|
151 |
+
"eval_accuracy": 0.8978835978835978,
|
152 |
+
"eval_f1": 0.8974625900835275,
|
153 |
+
"eval_loss": 0.48289522528648376,
|
154 |
+
"eval_precision": 0.8983196617688776,
|
155 |
+
"eval_recall": 0.8981742359077385,
|
156 |
+
"eval_runtime": 113.6055,
|
157 |
+
"eval_samples_per_second": 33.273,
|
158 |
+
"eval_steps_per_second": 4.164,
|
159 |
+
"step": 6618
|
160 |
+
},
|
161 |
+
{
|
162 |
+
"epoch": 6.0,
|
163 |
+
"step": 6618,
|
164 |
+
"total_flos": 1.39288377102336e+16,
|
165 |
+
"train_loss": 0.7601337173475748,
|
166 |
+
"train_runtime": 7219.9118,
|
167 |
+
"train_samples_per_second": 14.659,
|
168 |
+
"train_steps_per_second": 1.833
|
169 |
+
},
|
170 |
+
{
|
171 |
+
"epoch": 6.0,
|
172 |
+
"eval_accuracy": 0.8978835978835978,
|
173 |
+
"eval_f1": 0.8974625900835275,
|
174 |
+
"eval_loss": 0.48289522528648376,
|
175 |
+
"eval_precision": 0.8983196617688776,
|
176 |
+
"eval_recall": 0.8981742359077385,
|
177 |
+
"eval_runtime": 113.7531,
|
178 |
+
"eval_samples_per_second": 33.23,
|
179 |
+
"eval_steps_per_second": 4.158,
|
180 |
+
"step": 6618
|
181 |
+
},
|
182 |
+
{
|
183 |
+
"epoch": 6.0,
|
184 |
+
"step": 6618,
|
185 |
+
"train_en_eval_accuracy": 0.9667800453514739,
|
186 |
+
"train_en_eval_f1": 0.9665532935289809,
|
187 |
+
"train_en_eval_loss": 0.13728319108486176,
|
188 |
+
"train_en_eval_precision": 0.9668769668275939,
|
189 |
+
"train_en_eval_recall": 0.9665372917440072,
|
190 |
+
"train_en_loss": 0.13728320598602295,
|
191 |
+
"train_en_runtime": 265.3662,
|
192 |
+
"train_en_samples_per_second": 33.237,
|
193 |
+
"train_en_steps_per_second": 4.157
|
194 |
+
},
|
195 |
+
{
|
196 |
+
"epoch": 6.0,
|
197 |
+
"step": 6618,
|
198 |
+
"test_en_eval_accuracy": 0.8978835978835978,
|
199 |
+
"test_en_eval_f1": 0.8974625900835275,
|
200 |
+
"test_en_eval_loss": 0.48289522528648376,
|
201 |
+
"test_en_eval_precision": 0.8983196617688776,
|
202 |
+
"test_en_eval_recall": 0.8981742359077385,
|
203 |
+
"test_en_loss": 0.48289522528648376,
|
204 |
+
"test_en_runtime": 113.6677,
|
205 |
+
"test_en_samples_per_second": 33.255,
|
206 |
+
"test_en_steps_per_second": 4.161
|
207 |
+
}
|
208 |
+
],
|
209 |
+
"logging_steps": 500,
|
210 |
+
"max_steps": 13236,
|
211 |
+
"num_input_tokens_seen": 0,
|
212 |
+
"num_train_epochs": 12,
|
213 |
+
"save_steps": 500,
|
214 |
+
"stateful_callbacks": {
|
215 |
+
"TrainerControl": {
|
216 |
+
"args": {
|
217 |
+
"should_epoch_stop": false,
|
218 |
+
"should_evaluate": false,
|
219 |
+
"should_log": false,
|
220 |
+
"should_save": true,
|
221 |
+
"should_training_stop": true
|
222 |
+
},
|
223 |
+
"attributes": {}
|
224 |
+
}
|
225 |
+
},
|
226 |
+
"total_flos": 1.39288377102336e+16,
|
227 |
+
"train_batch_size": 8,
|
228 |
+
"trial_name": null,
|
229 |
+
"trial_params": null
|
230 |
+
}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:db8ac7d0511f62b94e66f0ef14455bd4f4dce508780d8fa019f57c88513cd5e9
|
3 |
+
size 5176
|