DISCLAIMER: **For those of you who are downloading this model, it is not finished, the results are poor.** 



Question Answering Model applying fine tuning to a GPT2 text generator model in a Catalan Dataset "projecte-aina/catalanqa".

Results over the first epoch

200it [01:14,  2.29it/s]Train: wpb=10, num_updates=200, accuracy=2.5, loss=0.97

500it [02:57,  3.06it/s]Train: wpb=10, num_updates=500, accuracy=3.1, loss=0.98

1000it [05:47,  2.72it/s]Train: wpb=10, num_updates=1000, accuracy=3.7, loss=0.91

2000it [11:29,  3.32it/s]Train: wpb=10, num_updates=2000, accuracy=3.7, loss=0.85

3000it [16:48,  3.90it/s]Train: wpb=10, num_updates=3000, accuracy=3.7, loss=0.82

4000it [22:10,  3.06it/s]Train: wpb=10, num_updates=4000, accuracy=3.9, loss=0.79

5000it [27:24,  3.50it/s]Train: wpb=10, num_updates=5000, accuracy=4.1, loss=0.77

6000it [32:41,  2.19it/s]Train: wpb=10, num_updates=6000, accuracy=4.5, loss=0.76

7000it [37:56,  3.03it/s]Train: wpb=10, num_updates=7000, accuracy=4.6, loss=0.75

8000it [43:06,  3.73it/s]Train: wpb=10, num_updates=8000, accuracy=4.8, loss=0.74

9000it [48:28,  2.85it/s]Train: wpb=10, num_updates=9000, accuracy=4.9, loss=0.73

10000it [53:43,  2.89it/s]Train: wpb=10, num_updates=10000, accuracy=5.1, loss=0.73

11000it [59:09,  3.10it/s]Train: wpb=10, num_updates=11000, accuracy=5.2, loss=0.73

12000it [1:04:37,  2.64it/s]Train: wpb=10, num_updates=12000, accuracy=5.3, loss=0.72

13000it [1:10:02,  2.66it/s]Train: wpb=10, num_updates=13000, accuracy=5.4, loss=0.72

14000it [1:15:15,  2.68it/s]Train: wpb=10, num_updates=14000, accuracy=5.4, loss=0.72

14150it [1:16:05,  3.10it/s]

Train: wpb=9, num_updates=14150, accuracy=5.4, loss=0.72

| epoch 000 | train accuracy=5.4%, train loss=0.72

| epoch 000 | valid accuracy=7.6%, valid loss=0.69


200it [01:16,  2.21it/s]Train: wpb=10, num_updates=200, accuracy=4.5, loss=0.68

500it [03:02,  2.94it/s]Train: wpb=10, num_updates=500, accuracy=4.3, loss=0.74

1000it [05:59,  2.60it/s]Train: wpb=10, num_updates=1000, accuracy=4.9, loss=0.74

2000it [11:53,  3.18it/s]Train: wpb=10, num_updates=2000, accuracy=4.8, loss=0.74

3000it [17:24,  3.80it/s]Train: wpb=10, num_updates=3000, accuracy=4.8, loss=0.73

4000it [22:58,  2.96it/s]Train: wpb=10, num_updates=4000, accuracy=4.9, loss=0.72

5000it [28:23,  3.43it/s]Train: wpb=10, num_updates=5000, accuracy=5.0, loss=0.71

6000it [33:52,  2.15it/s]Train: wpb=10, num_updates=6000, accuracy=5.2, loss=0.70

7000it [39:18,  2.92it/s]Train: wpb=10, num_updates=7000, accuracy=5.3, loss=0.70

8000it [44:39,  3.63it/s]Train: wpb=10, num_updates=8000, accuracy=5.4, loss=0.69

9000it [50:13,  2.74it/s]Train: wpb=10, num_updates=9000, accuracy=5.5, loss=0.69

10000it [55:39,  2.84it/s]Train: wpb=10, num_updates=10000, accuracy=5.7, loss=0.69

11000it [1:01:16,  3.00it/s]Train: wpb=10, num_updates=11000, accuracy=5.7, loss=0.69

12000it [1:06:57,  2.54it/s]Train: wpb=10, num_updates=12000, accuracy=5.8, loss=0.69

13000it [1:12:33,  2.56it/s]Train: wpb=10, num_updates=13000, accuracy=5.8, loss=0.69

14000it [1:17:58,  2.56it/s]Train: wpb=10, num_updates=14000, accuracy=5.9, loss=0.69

14150it [1:18:49,  2.99it/s]

Train: wpb=9, num_updates=14150, accuracy=5.9, loss=0.69

| epoch 001 | train accuracy=5.9%, train loss=0.69

| epoch 001 | valid accuracy=7.7%, valid loss=0.69
{}
transformers pytorch gpt2 text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

DISCLAIMER: For those of you who are downloading this model, it is not finished, the results are poor. 



Question Answering Model applying fine tuning to a GPT2 text generator model in a Catalan Dataset "projecte-aina/catalanqa".

Results over the first epoch

200it [01:14,  2.29it/s]Train: wpb=10, num_updates=200, accuracy=2.5, loss=0.97

500it [02:57,  3.06it/s]Train: wpb=10, num_updates=500, accuracy=3.1, loss=0.98

1000it [05:47,  2.72it/s]Train: wpb=10, num_updates=1000, accuracy=3.7, loss=0.91

2000it [11:29,  3.32it/s]Train: wpb=10, num_updates=2000, accuracy=3.7, loss=0.85

3000it [16:48,  3.90it/s]Train: wpb=10, num_updates=3000, accuracy=3.7, loss=0.82

4000it [22:10,  3.06it/s]Train: wpb=10, num_updates=4000, accuracy=3.9, loss=0.79

5000it [27:24,  3.50it/s]Train: wpb=10, num_updates=5000, accuracy=4.1, loss=0.77

6000it [32:41,  2.19it/s]Train: wpb=10, num_updates=6000, accuracy=4.5, loss=0.76

7000it [37:56,  3.03it/s]Train: wpb=10, num_updates=7000, accuracy=4.6, loss=0.75

8000it [43:06,  3.73it/s]Train: wpb=10, num_updates=8000, accuracy=4.8, loss=0.74

9000it [48:28,  2.85it/s]Train: wpb=10, num_updates=9000, accuracy=4.9, loss=0.73

10000it [53:43,  2.89it/s]Train: wpb=10, num_updates=10000, accuracy=5.1, loss=0.73

11000it [59:09,  3.10it/s]Train: wpb=10, num_updates=11000, accuracy=5.2, loss=0.73

12000it [1:04:37,  2.64it/s]Train: wpb=10, num_updates=12000, accuracy=5.3, loss=0.72

13000it [1:10:02,  2.66it/s]Train: wpb=10, num_updates=13000, accuracy=5.4, loss=0.72

14000it [1:15:15,  2.68it/s]Train: wpb=10, num_updates=14000, accuracy=5.4, loss=0.72

14150it [1:16:05,  3.10it/s]

Train: wpb=9, num_updates=14150, accuracy=5.4, loss=0.72

| epoch 000 | train accuracy=5.4%, train loss=0.72

| epoch 000 | valid accuracy=7.6%, valid loss=0.69


200it [01:16,  2.21it/s]Train: wpb=10, num_updates=200, accuracy=4.5, loss=0.68

500it [03:02,  2.94it/s]Train: wpb=10, num_updates=500, accuracy=4.3, loss=0.74

1000it [05:59,  2.60it/s]Train: wpb=10, num_updates=1000, accuracy=4.9, loss=0.74

2000it [11:53,  3.18it/s]Train: wpb=10, num_updates=2000, accuracy=4.8, loss=0.74

3000it [17:24,  3.80it/s]Train: wpb=10, num_updates=3000, accuracy=4.8, loss=0.73

4000it [22:58,  2.96it/s]Train: wpb=10, num_updates=4000, accuracy=4.9, loss=0.72

5000it [28:23,  3.43it/s]Train: wpb=10, num_updates=5000, accuracy=5.0, loss=0.71

6000it [33:52,  2.15it/s]Train: wpb=10, num_updates=6000, accuracy=5.2, loss=0.70

7000it [39:18,  2.92it/s]Train: wpb=10, num_updates=7000, accuracy=5.3, loss=0.70

8000it [44:39,  3.63it/s]Train: wpb=10, num_updates=8000, accuracy=5.4, loss=0.69

9000it [50:13,  2.74it/s]Train: wpb=10, num_updates=9000, accuracy=5.5, loss=0.69

10000it [55:39,  2.84it/s]Train: wpb=10, num_updates=10000, accuracy=5.7, loss=0.69

11000it [1:01:16,  3.00it/s]Train: wpb=10, num_updates=11000, accuracy=5.7, loss=0.69

12000it [1:06:57,  2.54it/s]Train: wpb=10, num_updates=12000, accuracy=5.8, loss=0.69

13000it [1:12:33,  2.56it/s]Train: wpb=10, num_updates=13000, accuracy=5.8, loss=0.69

14000it [1:17:58,  2.56it/s]Train: wpb=10, num_updates=14000, accuracy=5.9, loss=0.69

14150it [1:18:49,  2.99it/s]

Train: wpb=9, num_updates=14150, accuracy=5.9, loss=0.69

| epoch 001 | train accuracy=5.9%, train loss=0.69

| epoch 001 | valid accuracy=7.7%, valid loss=0.69
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
